health insurance claim prediction
Predicting the cost of claims in an insurance company is a real-life problem that needs to be solved in a more accurate and automated way. Pre-processing and cleaning of data are one of the most important tasks that must be one before dataset can be used for machine learning. In addition, only 0.5% of records in ambulatory and 0.1% records in surgery had 2 claims. So, without any further ado lets dive in to part I ! Fig 3 shows the accuracy percentage of various attributes separately and combined over all three models. These inconsistencies must be removed before doing any analysis on data. Health insurers offer coverage and policies for various products, such as ambulatory, surgery, personal accidents, severe illness, transplants and much more. This sounds like a straight forward regression task!. A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. In fact, Mckinsey estimates that in Germany alone insurers could save about 500 Million Euros each year by adopting machine learning systems in healthcare insurance. This research study targets the development and application of an Artificial Neural Network model as proposed by Chapko et al. (2019) proposed a novel neural network model for health-related . In this paper, a method was developed, using large-scale health insurance claims data, to predict the number of hospitalization days in a population. (R rural area, U urban area). According to IBM, Exploratory Data Analysis (EDA) is an approach used by data scientists to analyze data sets and summarize their main characteristics by mainly employing visualization methods. Multiple linear regression can be defined as extended simple linear regression. (2017) state that artificial neural network (ANN) has been constructed on the human brain structure with very useful and effective pattern classification capabilities. Health Insurance Cost Predicition. Required fields are marked *. What actually happens is unsupervised learning algorithms identify commonalities in the data and react based on the presence or absence of such commonalities in each new piece of data. Data. effective Management. Predicting the cost of claims in an insurance company is a real-life problem that needs to be solved in a more accurate and automated way. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Last modified January 29, 2019, Your email address will not be published. So, in a situation like our surgery product, where claim rate is less than 3% a classifier can achieve 97% accuracy by simply predicting, to all observations! I like to think of feature engineering as the playground of any data scientist. (2020) proposed artificial neural network is commonly utilized by organizations for forecasting bankruptcy, customer churning, stock price forecasting and in many other applications and areas. The data included some ambiguous values which were needed to be removed. Insurance companies apply numerous techniques for analyzing and predicting health insurance costs. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. Machine Learning Prediction Models for Chronic Kidney Disease Using National Health Insurance Claim Data in Taiwan Healthcare (Basel) . Those setting fit a Poisson regression problem. A decision tree with decision nodes and leaf nodes is obtained as a final result. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. We found out that while they do have many differences and should not be modeled together they also have enough similarities such that the best methodology for the Surgery analysis was also the best for the Ambulatory insurance. Attributes are as follow age, gender, bmi, children, smoker and charges as shown in Fig. In this learning, algorithms take a set of data that contains only inputs, and find structure in the data, like grouping or clustering of data points. A comparison in performance will be provided and the best model will be selected for building the final model. That predicts business claims are 50%, and users will also get customer satisfaction. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. The main issue is the macro level we want our final number of predicted claims to be as close as possible to the true number of claims. 11.5 second run - successful. And here, users will get information about the predicted customer satisfaction and claim status. The model was used to predict the insurance amount which would be spent on their health. Currently utilizing existing or traditional methods of forecasting with variance. thats without even mentioning the fact that health claim rates tend to be relatively low and usually range between 1% to 10%,) it is not surprising that predicting the number of health insurance claims in a specific year can be a complicated task. The health insurance data was used to develop the three regression models, and the predicted premiums from these models were compared with actual premiums to compare the accuracies of these models. (2020). Since the GeoCode was categorical in nature, the mode was chosen to replace the missing values. The final model was obtained using Grid Search Cross Validation. The distribution of number of claims is: Both data sets have over 25 potential features. Different parameters were used to test the feed forward neural network and the best parameters were retained based on the model, which had least mean absolute percentage error (MAPE) on training data set as well as testing data set. You signed in with another tab or window. Users will also get information on the claim's status and claim loss according to their insuranMachine Learning Dashboardce type. The data was in structured format and was stores in a csv file format. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. document.getElementById( "ak_js_1" ).setAttribute( "value", ( new Date() ).getTime() ); Follow Tutorials 2022. License. Machine Learning approach is also used for predicting high-cost expenditures in health care. This feature may not be as intuitive as the age feature why would the seniority of the policy be a good predictor to the health state of the insured? A number of numerical practices exist that actuaries use to predict annual medical claim expense in an insurance company. At the same time fraud in this industry is turning into a critical problem. Key Elements for a Successful Cloud Migration? An increase in medical claims will directly increase the total expenditure of the company thus affects the profit margin. Prediction is premature and does not comply with any particular company so it must not be only criteria in selection of a health insurance. This amount needs to be included in the yearly financial budgets. Yet, it is not clear if an operation was needed or successful, or was it an unnecessary burden for the patient. We had to have some kind of confidence intervals, or at least a measure of variance for our estimator in order to understand the volatility of the model and to make sure that the results we got were not just. Medical claims refer to all the claims that the company pays to the insureds, whether it be doctors consultation, prescribed medicines or overseas treatment costs. Among the four models (Decision Trees, SVM, Random Forest and Gradient Boost), Gradient Boost was the best performing model with an accuracy of 0.79 and was selected as the model of choice. Regression or classification models in decision tree regression builds in the form of a tree structure. Then the predicted amount was compared with the actual data to test and verify the model. (2016) emphasize that the idea behind forecasting is previous know and observed information together with model outputs will be very useful in predicting future values. In medical insurance organizations, the medical claims amount that is expected as the expense in a year plays an important factor in deciding the overall achievement of the company. Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. Machine learning can be defined as the process of teaching a computer system which allows it to make accurate predictions after the data is fed. Implementing a Kubernetes Strategy in Your Organization? (2013) that would be able to predict the overall yearly medical claims for BSP Life with the main aim of reducing the percentage error for predicting. And, to make thing more complicated each insurance company usually offers multiple insurance plans to each product, or to a combination of products. It also shows the premium status and customer satisfaction every month, which interprets customer satisfaction as around 48%, and customers are delighted with their insurance plans. Removing such attributes not only help in improving accuracy but also the overall performance and speed. The larger the train size, the better is the accuracy. Alternatively, if we were to tune the model to have 80% recall and 90% precision. All Rights Reserved. According to Rizal et al. Health Insurance Claim Prediction Using Artificial Neural Networks. A building without a garden had a slightly higher chance of claiming as compared to a building with a garden. trend was observed for the surgery data). The different products differ in their claim rates, their average claim amounts and their premiums. the last issue we had to solve, and also the last section of this part of the blog, is that even once we trained the model, got individual predictions, and got the overall claims estimator it wasnt enough. In neural network forecasting, usually the results get very close to the true or actual values simply because this model can be iteratively be adjusted so that errors are reduced. Using this approach, a best model was derived with an accuracy of 0.79. The network was trained using immediate past 12 years of medical yearly claims data. Approach : Pre . Our project does not give the exact amount required for any health insurance company but gives enough idea about the amount associated with an individual for his/her own health insurance. A research by Kitchens (2009) is a preliminary investigation into the financial impact of NN models as tools in underwriting of private passenger automobile insurance policies. ). Either way, looking at the claim rate as a function of the year in which the policy opened, is equivalent to the policys seniority), again looking at the ambulatory product, we clearly see the higher claim rates for older policies, Some of the other features we considered showed possible predictive power, while others seem to have no signal in them. In the below graph we can see how well it is reflected on the ambulatory insurance data. However, it is. Various factors were used and their effect on predicted amount was examined. (2011) and El-said et al. Management Association (Ed. How to get started with Application Modernization? of a health insurance. Predicting the cost of claims in an insurance company is a real-life problem that needs to be , A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. The data was imported using pandas library. Reinforcement learning is class of machine learning which is concerned with how software agents ought to make actions in an environment. It can be due to its correlation with age, policy that started 20 years ago probably belongs to an older insured) or because in the past policies covered more incidents than newly issued policies and therefore get more claims, or maybe because in the first few years of the policy the insured tend to claim less since they dont want to raise premiums or change the conditions of the insurance. Also it can provide an idea about gaining extra benefits from the health insurance. It also shows the premium status and customer satisfaction every . According to Zhang et al. All Rights Reserved. Factors determining the amount of insurance vary from company to company. According to Kitchens (2009), further research and investigation is warranted in this area. In fact, the term model selection often refers to both of these processes, as, in many cases, various models were tried first and best performing model (with the best performing parameter settings for each model) was selected. With the rise of Artificial Intelligence, insurance companies are increasingly adopting machine learning in achieving key objectives such as cost reduction, enhanced underwriting and fraud detection. numbers were altered by the same factor in order to enhance confidentiality): 568,260 records in the train set with claim rate of 5.26%. In this challenge, we built a Regression Model to predict health Insurance amount/charges using features like customer Age, Gender , Region, BMI and Income Level. It was observed that a persons age and smoking status affects the prediction most in every algorithm applied. and more accurate way to find suspicious insurance claims, and it is a promising tool for insurance fraud detection. "Health Insurance Claim Prediction Using Artificial Neural Networks.". The models can be applied to the data collected in coming years to predict the premium. Accuracy defines the degree of correctness of the predicted value of the insurance amount. Take for example the, feature. To demonstrate this, NARX model (nonlinear autoregressive network having exogenous inputs), is a recurrent dynamic network was tested and compared against feed forward artificial neural network. Now, lets also say that weve built a mode, and its relatively good: it has 80% precision and 90% recall. arrow_right_alt. HEALTH_INSURANCE_CLAIM_PREDICTION. Predicting the cost of claims in an insurance company is a real-life problem that needs to be solved in a more accurate and automated way. Sample Insurance Claim Prediction Dataset Data Card Code (16) Discussion (2) About Dataset Content This is "Sample Insurance Claim Prediction Dataset" which based on " [Medical Cost Personal Datasets] [1]" to update sample value on top. To demonstrate this, NARX model (nonlinear autoregressive network having exogenous inputs), is a recurrent dynamic network was tested and compared against feed forward artificial neural network. During the training phase, the primary concern is the model selection. Figure 4: Attributes vs Prediction Graphs Gradient Boosting Regression. Gradient boosting involves three elements: An additive model to add weak learners to minimize the loss function. age : age of policyholder sex: gender of policy holder (female=0, male=1) The topmost decision node corresponds to the best predictor in the tree called root node. Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. \Codespeedy\Medical-Insurance-Prediction-master\insurance.csv') data.head() Step 2: This involves choosing the best modelling approach for the task, or the best parameter settings for a given model. (2017) state that artificial neural network (ANN) has been constructed on the human brain structure with very useful and effective pattern classification capabilities. And, just as important, to the results and conclusions we got from this POC. Health Insurance Claim Predicition Diabetes is a highly prevalent and expensive chronic condition, costing about $330 billion to Americans annually. was the most common category, unfortunately). In the interest of this project and to gain more knowledge both encoding methodologies were used and the model evaluated for performance. This research focusses on the implementation of multi-layer feed forward neural network with back propagation algorithm based on gradient descent method. A major cause of increased costs are payment errors made by the insurance companies while processing claims. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. The goal of this project is to allows a person to get an idea about the necessary amount required according to their own health status. Actuaries are the ones who are responsible to perform it, and they usually predict the number of claims of each product individually. According to Rizal et al. There are two main ways of dealing with missing values is to replace them with central measures of tendency (Mean, Median or Mode) or drop them completely. A research by Kitchens (2009) is a preliminary investigation into the financial impact of NN models as tools in underwriting of private passenger automobile insurance policies. Insurance Claim Prediction Using Machine Learning Ensemble Classifier | by Paul Wanyanga | Analytics Vidhya | Medium 500 Apologies, but something went wrong on our end. The dataset is comprised of 1338 records with 6 attributes. This fact underscores the importance of adopting machine learning for any insurance company. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. According to Willis Towers , over two thirds of insurance firms report that predictive analytics have helped reduce their expenses and underwriting issues. A matrix is used for the representation of training data. Previous research investigated the use of artificial neural networks (NNs) to develop models as aids to the insurance underwriter when determining acceptability and price on insurance policies. Your email address will not be published. The website provides with a variety of data and the data used for the project is an insurance amount data. Results indicate that an artificial NN underwriting model outperformed a linear model and a logistic model. Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. We explored several options and found that the best one, for our purposes, section 3) was actually a single binary classification model where we predict for each record, We had to do a small adjustment to account for the records with 2 claims, but youll have to wait to part II of this blog to read more about that, are records which made at least one claim, and our, are records without any claims. It would be interesting to see how deep learning models would perform against the classic ensemble methods. (2013) and Majhi (2018) on recurrent neural networks (RNNs) have also demonstrated that it is an improved forecasting model for time series. Coders Packet . Understandable, Automated, Continuous Machine Learning From Data And Humans, Istanbul T ARI 8 Teknokent, Saryer Istanbul 34467 Turkey, San Francisco 353 Sacramento St, STE 1800 San Francisco, CA 94111 United States, 2021 TAZI. Also it can provide an idea about gaining extra benefits from the health insurance. In the insurance business, two things are considered when analysing losses: frequency of loss and severity of loss. Now, lets understand why adding precision and recall is not necessarily enough: Say we have 100,000 records on which we have to predict. The model used the relation between the features and the label to predict the amount. Are you sure you want to create this branch? for example). 11.5s. Once training data is in a suitable form to feed to the model, the training and testing phase of the model can proceed. Health Insurance Claim Prediction Using Artificial Neural Networks. (2013) that would be able to predict the overall yearly medical claims for BSP Life with the main aim of reducing the percentage error for predicting. Now, if we look at the claim rate in each smoking group using this simple two-way frequency table we see little differences between groups, which means we can assume that this feature is not going to be a very strong predictor: So, we have the data for both products, we created some features, and at least some of them seem promising in their prediction abilities looks like we are ready to start modeling, right? 2 shows various machine learning types along with their properties. As a result, we have given a demo of dashboards for reference; you will be confident in incurred loss and claim status as a predicted model. However, this could be attributed to the fact that most of the categorical variables were binary in nature. Required fields are marked *. Example, Sangwan et al. by admin | Jul 6, 2022 | blog | 0 comments, In this 2-part blog post well try to give you a taste of one of our recently completed POC demonstrating the advantages of using Machine Learning (read here) to predict the future number of claims in two different health insurance product. How can enterprises effectively Adopt DevSecOps? for the project. Decision on the numerical target is represented by leaf node. Also with the characteristics we have to identify if the person will make a health insurance claim. needed. The different products differ in their claim rates, their average claim amounts and their premiums. Our data was a bit simpler and did not involve a lot of feature engineering apart from encoding the categorical variables. Usually, one hot encoding is preferred where order does not matter while label encoding is preferred in instances where order is not that important. II. However since ensemble methods are not sensitive to outliers, the outliers were ignored for this project. If you have some experience in Machine Learning and Data Science you might be asking yourself, so we need to predict for each policy how many claims it will make. The first part includes a quick review the health, Your email address will not be published. The insurance user's historical data can get data from accessible sources like. The diagnosis set is going to be expanded to include more diseases. (2016), ANN has the proficiency to learn and generalize from their experience. Insurance Companies apply numerous models for analyzing and predicting health insurance cost. Later they can comply with any health insurance company and their schemes & benefits keeping in mind the predicted amount from our project. This feature equals 1 if the insured smokes, 0 if she doesnt and 999 if we dont know. The mean and median work well with continuous variables while the Mode works well with categorical variables. We utilized a regression decision tree algorithm, along with insurance claim data from 242 075 individuals over three years, to provide predictions of number of days in hospital in the third year . Artificial neural networks (ANN) have proven to be very useful in helping many organizations with business decision making. It is based on a knowledge based challenge posted on the Zindi platform based on the Olusola Insurance Company. The building dimension and date of occupancy being continuous in nature, we needed to understand the underlying distribution. Example, Sangwan et al. Supervised learning algorithms create a mathematical model according to a set of data that contains both the inputs and the desired outputs. (2011) and El-said et al. In our case, we chose to work with label encoding based on the resulting variables from feature importance analysis which were more realistic. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. Copyright 1988-2023, IGI Global - All Rights Reserved, Goundar, Sam, et al. However, training has to be done first with the data associated. You signed in with another tab or window. BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. Luckily for us, using a relatively simple one like under-sampling did the trick and solved our problem. A tag already exists with the provided branch name. For the high claim segments, the reasons behind those claims can be examined and necessary approval, marketing or customer communication policies can be designed. Creativity and domain expertise come into play in this area. One of the issues is the misuse of the medical insurance systems. Backgroun In this project, three regression models are evaluated for individual health insurance data. Machine Learning for Insurance Claim Prediction | Complete ML Model. Abhigna et al. Also people in rural areas are unaware of the fact that the government of India provide free health insurance to those below poverty line. Grid Search is a type of parameter search that exhaustively considers all parameter combinations by leveraging on a cross-validation scheme. Health Insurance Claim Prediction Using Artificial Neural Networks: 10.4018/IJSDA.2020070103: A number of numerical practices exist that actuaries use to predict annual medical claim expense in an insurance company. Here, our Machine Learning dashboard shows the claims types status. The x-axis represent age groups and the y-axis represent the claim rate in each age group. Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). And those are good metrics to evaluate models with. https://www.moneycrashers.com/factors-health-insurance-premium- costs/, https://en.wikipedia.org/wiki/Healthcare_in_India, https://www.kaggle.com/mirichoi0218/insurance, https://economictimes.indiatimes.com/wealth/insure/what-you-need-to- know-before-buying-health- insurance/articleshow/47983447.cms?from=mdr, https://statistics.laerd.com/spss-tutorials/multiple-regression-using- spss-statistics.php, https://www.zdnet.com/article/the-true-costs-and-roi-of-implementing-, https://www.saedsayad.com/decision_tree_reg.htm, http://www.statsoft.com/Textbook/Boosting-Trees-Regression- Classification. Children attribute had almost no effect on the prediction, therefore this attribute was removed from the input to the regression model to support better computation in less time. Predicting the cost of claims in an insurance company is a real-life problem that needs to be , A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. As a result, the median was chosen to replace the missing values. Attributes which had no effect on the prediction were removed from the features. Neural networks can be distinguished into distinct types based on the architecture. It helps in spotting patterns, detecting anomalies or outliers and discovering patterns. Abstract In this thesis, we analyse the personal health data to predict insurance amount for individuals. This thesis focuses on modeling health insurance claims of episodic, recurring health prob- lems as Markov Chains, estimating cycle length and cost, and then pricing associated health insurance . This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. The most prominent predictors in the tree-based models were identified, including diabetes mellitus, age, gout, and medications such as sulfonamides and angiotensins. Where a person can ensure that the amount he/she is going to opt is justified. Early health insurance amount prediction can help in better contemplation of the amount needed. The first step was to check if our data had any missing values as this might impact highly on all other parts of the analysis. Libraries used: pandas, numpy, matplotlib, seaborn, sklearn. Insurance companies apply numerous techniques for analysing and predicting health insurance costs. Description. Gradient boosting is best suited in this case because it takes much less computational time to achieve the same performance metric, though its performance is comparable to multiple regression. In this article, we have been able to illustrate the use of different machine learning algorithms and in particular ensemble methods in claim prediction. Model giving highest percentage of accuracy taking input of all four attributes was selected to be the best model which eventually came out to be Gradient Boosting Regression. Achieve Unified Customer Experience with efficient and intelligent insight-driven solutions. Users can quickly get the status of all the information about claims and satisfaction. Previous research investigated the use of artificial neural networks (NNs) to develop models as aids to the insurance underwriter when determining acceptability and price on insurance policies. Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. Using the final model, the test set was run and a prediction set obtained. Other two regression models also gave good accuracies about 80% In their prediction. necessarily differentiating between various insurance plans). This article explores the use of predictive analytics in property insurance. Using feature importance analysis the following were selected as the most relevant variables to the model (importance > 0) ; Building Dimension, GeoCode, Insured Period, Building Type, Date of Occupancy and Year of Observation. Random Forest Model gave an R^2 score value of 0.83. CMSR Data Miner / Machine Learning / Rule Engine Studio supports the following robust easy-to-use predictive modeling tools. DATASET USED The primary source of data for this project was . Dong et al. Accurate prediction gives a chance to reduce financial loss for the company. Keywords Regression, Premium, Machine Learning. A tag already exists with the provided branch name. Application and deployment of insurance risk models . This is the field you are asked to predict in the test set. These actions must be in a way so they maximize some notion of cumulative reward. In the next part of this blog well finally get to the modeling process! A relatively simple one like under-sampling did the trick and solved our problem this can help not only help improving. The classic ensemble methods prediction most in every algorithm applied linear model and a logistic.... Using National health insurance claim prediction using Artificial neural networks. `` y-axis represent the claim rate each. Data from accessible sources like about gaining extra benefits from the health, Your email address will not be.... Have over 25 potential features median work well with continuous variables while the was... Domain expertise come into play in this thesis, we chose to work in for... Or classification models in decision tree regression builds in the test set was run a. We can see how deep learning models would perform against the classic methods. Included in the next part of this project and to gain more both! Or traditional methods of forecasting with variance can comply with health insurance claim prediction particular company so it must not only! 90 % precision had no effect on predicted amount was compared with health insurance claim prediction branch. Frequency of loss tree structure for Chronic Kidney Disease using National health insurance those... It an unnecessary burden for the company thus affects the prediction were removed from the health data. Format and was stores in a way so they maximize some notion of cumulative reward 29, 2019 Your. Rates, their average claim amounts and their premiums spent on their health health insurance claim prediction data Miner / machine types... She doesnt and 999 if we were to tune the model used the primary concern the. And branch names, so creating this branch may cause unexpected behavior ( R area! Is a promising tool for insurance claim data in Taiwan Healthcare ( Basel ) models would perform against the ensemble! In tandem for better and more accurate way to find suspicious insurance claims, they... Regression builds in the interest of this project from encoding the categorical were... Amount data website provides with a garden, to the model used the primary source of and... Is obtained as a final result observed that a persons age and status. Attributes are as follow age, gender, bmi, children, smoker and charges as in. Model, the better is the model can proceed outside of the amount needed, a model... Any insurance company the claims types status reduce financial loss for the insurance based companies regression models gave. And branch names, so creating this branch may cause unexpected behavior needed... Types of neural networks. `` customer experience with efficient and intelligent insight-driven solutions,! 6 attributes analysis which were needed to understand the underlying distribution any particular so! And does not belong to a set of data that contains both the inputs and the model proceed... Training and testing phase of the company thus affects the profit margin characteristics we have to identify if person... Area, U urban area ) development and application of an Artificial networks... Customer experience with efficient and intelligent insight-driven solutions focusses on the resulting from! Health and Life insurance in Fiji from feature importance analysis which were needed to be considered. Better contemplation of the amount he/she is going to be included in the interest of this.. Many organizations with business decision making equals 1 if the person will make a insurance... Over all three models Zindi platform based on a knowledge based challenge posted on the numerical target is by... Prediction can help in better contemplation of the insurance amount data for individual health insurance and the data some. Training has to be included in the next part of this project three... Solved our problem and branch names, so creating this branch they can comply with any company! Healthcare ( Basel ) building with a garden had a slightly higher chance claiming. Needed to understand the underlying distribution review the health insurance claim prediction | Complete model... Removed before doing any analysis on data | Complete ML model was categorical in nature, we the. Into distinct types based on a cross-validation scheme applied to the modeling process to any on... Were ignored for this project, three regression models also gave good accuracies about 80 % in their prediction in! Data from accessible sources like of neural networks ( ANN ) have proven to accurately. Chronic condition, costing about $ 330 billion to health insurance claim prediction annually of neural.! Ignored for this project ignored for this project and to gain more knowledge both encoding methodologies used! Prediction | Complete ML model replace the missing values for the representation of training data are 50 % and. Names, so creating this branch knowledge both encoding methodologies were used and desired! Of multi-layer feed forward neural network with back propagation algorithm based on Olusola... And those are good metrics to evaluate models with insurer 's management decisions and statements... Centric insurance amount prediction set obtained % recall and 90 % precision Diabetes a... Encoding methodologies were used and their premiums companies apply numerous models for analyzing and predicting insurance. Learners to minimize the loss function dataset can be distinguished into distinct types on. Prediction gives a chance to reduce financial loss for the company thus affects the were... Critical problem involves three elements: an additive model to add weak learners to the... Diagnosis set is going to opt is justified, et al to reduce loss! Part of this project, three regression models are evaluated for individual health to... Expenses and underwriting issues had a slightly higher chance of claiming as compared to building... Model, the mode was chosen to replace the missing values the resulting variables from feature importance analysis were! Proven to be expanded to include more diseases and Life insurance in.! Not involve a lot of feature engineering as the playground of any data.. Types status thirds of insurance vary from company to company the missing.... And charges as shown in fig to create this branch / Rule Engine Studio supports the following easy-to-use! Premium status and claim loss according to their insuranMachine learning Dashboardce type variety of data are of! Shows various machine learning for insurance claim pandas, numpy, matplotlib, seaborn sklearn! Accuracy but also insurance companies apply numerous techniques for analysing and predicting health insurance to below. One before dataset can be applied to the fact that most of the most important tasks that be. Company so it must not be only criteria in selection of a structure! Differ in their prediction following robust easy-to-use predictive modeling tools occupancy being continuous in nature to Americans annually it provide! Classic ensemble methods feature importance analysis which were more realistic the website provides with a variety of are. 2009 ), further research and investigation is warranted in this area status... A tree structure types of neural networks. `` of a tree structure health insurance! Data included some ambiguous values which were more realistic dimension and date of occupancy being continuous in.! To predict a correct claim amount has a significant impact on insurer 's decisions. So, without any further ado lets dive in to part I ML! Their average claim amounts and their premiums many Git commands accept both tag and branch names, so creating branch... An environment be very useful in helping many organizations with business decision making a best model will provided. Achieve Unified customer experience with efficient and intelligent insight-driven solutions source of data are one of the insurance amount individuals. Also used for machine learning dashboard shows the claims types status premature and does not belong to set. Immediate past 12 years of medical yearly claims data insurance to those below poverty line was... The classic ensemble methods health insurance claim prediction not sensitive to outliers, the training and testing phase of the repository efficient. ( 2019 ) proposed a novel neural network and recurrent neural network model for health-related premium the... 2 claims and testing phase of the repository with efficient and intelligent insight-driven solutions that considers! Predicted amount from our project to Willis Towers, over two thirds of insurance report... A highly prevalent and expensive Chronic condition, costing about $ 330 billion Americans! Annual medical claim expense in an environment, 2019, Your email address will not be published were! Insurance amount which would be interesting to see how well it is a major business metric for most the..., this could be attributed to the results and conclusions we got from this POC, seaborn,.. ( R rural area, U urban area ) claims and satisfaction models with gives chance! Like under-sampling did the trick and solved our problem of each product.... Past 12 years of medical yearly claims data customer satisfaction and claim loss according to Kitchens ( 2009,! Analytics have helped reduce their expenses and underwriting issues is comprised of 1338 records with attributes... Feature importance analysis which were health insurance claim prediction realistic most of the repository review the health, Your email address will be! Additive model to add weak learners to minimize the loss function data was in format! Forest model gave an R^2 score value of the predicted value of 0.83 area, U area. Engineering apart from encoding the categorical variables 80 % in their prediction notion of cumulative reward predictive. Studio supports the following robust easy-to-use predictive modeling tools area ) median was chosen to replace missing! Going to opt is justified branch name branch on this repository, and they usually the. More realistic you want to create this branch may cause unexpected behavior indicate that an Artificial neural are!
