health insurance claim prediction

PREDICTING HEALTH INSURANCE AMOUNT BASED ON FEATURES LIKE AGE, BMI , GENDER . Regression or classification models in decision tree regression builds in the form of a tree structure. Whats happening in the mathematical model is each training dataset is represented by an array or vector, known as a feature vector. Achieve Unified Customer Experience with efficient and intelligent insight-driven solutions. 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? 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. for example). Fig 3 shows the accuracy percentage of various attributes separately and combined over all three models. Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. Our data was a bit simpler and did not involve a lot of feature engineering apart from encoding the categorical variables. Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. This research focusses on the implementation of multi-layer feed forward neural network with back propagation algorithm based on gradient descent method. According to Kitchens (2009), further research and investigation is warranted in this area. Supervised learning algorithms learn from a model containing function that can be used to predict the output from the new inputs through iterative optimization of an objective function. Those setting fit a Poisson regression problem. Also it can provide an idea about gaining extra benefits from the health insurance. 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. A tag already exists with the provided branch name. That predicts business claims are 50%, and users will also get customer satisfaction. The network was trained using immediate past 12 years of medical yearly claims data. A matrix is used for the representation of training data. You signed in with another tab or window. Other two regression models also gave good accuracies about 80% In their prediction. Apart from this people can be fooled easily about the amount of the insurance and may unnecessarily buy some expensive health insurance. Dong et al. Example, Sangwan et al. This amount needs to be included in (2017) state that artificial neural network (ANN) has been constructed on the human brain structure with very useful and effective pattern classification capabilities. 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. According to Zhang et al. Machine Learning approach is also used for predicting high-cost expenditures in health care. A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. Are you sure you want to create this branch? Health Insurance Claim Prediction Using Artificial Neural Networks A. Bhardwaj Published 1 July 2020 Computer Science Int. 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. Some of the work investigated the predictive modeling of healthcare cost using several statistical techniques. CMSR Data Miner / Machine Learning / Rule Engine Studio supports the following robust easy-to-use predictive modeling tools. Dr. Akhilesh Das Gupta Institute of Technology & Management. 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. 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. Factors determining the amount of insurance vary from company to company. I like to think of feature engineering as the playground of any data scientist. The models can be applied to the data collected in coming years to predict the premium. 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. The size of the data used for training of data has a huge impact on the accuracy of data. insurance field, its unique settings and obstacles and the predictions required, and describes the data we had and the questions we had to ask ourselves before modeling. Dataset is not suited for the regression to take place directly. II. In the past, research by Mahmoud et al. There are many techniques to handle imbalanced data sets. 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. C Program Checker for Even or Odd Integer, Trivia Flutter App Project with Source Code, Flutter Date Picker Project with Source Code. This fact underscores the importance of adopting machine learning for any insurance company. 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. The model used the relation between the features and the label to predict the amount. This can help a person in focusing more on the health aspect of an insurance rather than the futile part. Again, for the sake of not ending up with the longest post ever, we wont go over all the features, or explain how and why we created each of them, but we can look at two exemplary features which are commonly used among actuaries in the field: age is probably the first feature most people would think of in the context of health insurance: we all know that the older we get, the higher is the probability of us getting sick and require medical attention. As you probably understood if you got this far our goal is to predict the number of claims for a specific product in a specific year, based on historic data. A building in the rural area had a slightly higher chance claiming as compared to a building in the urban area. 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 Notebook has been released under the Apache 2.0 open source license. Grid Search is a type of parameter search that exhaustively considers all parameter combinations by leveraging on a cross-validation scheme. In the insurance business, two things are considered when analysing losses: frequency of loss and severity of loss. In our case, we chose to work with label encoding based on the resulting variables from feature importance analysis which were more realistic. 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. 1. (2013) and Majhi (2018) on recurrent neural networks (RNNs) have also demonstrated that it is an improved forecasting model for time series. trend was observed for the surgery data). Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 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. 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. Required fields are marked *. The data has been imported from kaggle website. Health insurance is a necessity nowadays, and almost every individual is linked with a government or private health insurance company. Using this approach, a best model was derived with an accuracy of 0.79. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. 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. Also with the characteristics we have to identify if the person will make a health insurance claim. With Xenonstack Support, one can build accurate and predictive models on real-time data to better understand the customer for claims and satisfaction and their cost and premium. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. J. Syst. The data included various attributes such as age, gender, body mass index, smoker and the charges attribute which will work as the label. These actions must be in a way so they maximize some notion of cumulative reward. Save my name, email, and website in this browser for the next time I comment. insurance claim prediction machine learning. Going back to my original point getting good classification metric values is not enough in our case! 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. necessarily differentiating between various insurance plans). These inconsistencies must be removed before doing any analysis on data. 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. Once training data is in a suitable form to feed to the model, the training and testing phase of the model can proceed. 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. 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! Though unsupervised learning, encompasses other domains involving summarizing and explaining data features also. The effect of various independent variables on the premium amount was also checked. Backgroun In this project, three regression models are evaluated for individual health insurance data. \Codespeedy\Medical-Insurance-Prediction-master\insurance.csv') data.head() Step 2: The different products differ in their claim rates, their average claim amounts and their premiums. 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. 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. Creativity and domain expertise come into play in this area. In I. The larger the train size, the better is the accuracy. Data. A decision tree with decision nodes and leaf nodes is obtained as a final result. It is very complex method and some rural people either buy some private health insurance or do not invest money in health insurance at all. The predicted variable or the variable we want to predict is called the dependent variable (or sometimes, the outcome, target or criterion variable) and the variables being used in predict of the value of the dependent variable are called the independent variables (or sometimes, the predicto, explanatory or regressor variables). The mean and median work well with continuous variables while the Mode works well with categorical variables. 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. Approach : Pre . These claim amounts are usually high in millions of dollars every year. 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. Fig. Your email address will not be published. In particular using machine learning, insurers can be able to efficiently screen cases, evaluate them with great accuracy and make accurate cost predictions. Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. 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. A number of numerical practices exist that actuaries use to predict annual medical claim expense in an insurance company. Later the accuracies of these models were compared. 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. 1993, Dans 1993) because these databases are designed for nancial . The value of (health insurance) claims data in medical research has often been questioned (Jolins et al. The different products differ in their claim rates, their average claim amounts and their premiums. Fig. 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. Multiple linear regression can be defined as extended simple linear regression. The model proposed in this study could be a useful tool for policymakers in predicting the trends of CKD in the population. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. These claim amounts are usually high in millions of dollars every year. Random Forest Model gave an R^2 score value of 0.83. Health Insurance Claim Prediction Using Artificial Neural Networks. Bootstrapping our data and repeatedly train models on the different samples enabled us to get multiple estimators and from them to estimate the confidence interval and variance required. Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. an insurance plan that cover all ambulatory needs and emergency surgery only, up to $20,000). The data was in structured format and was stores in a csv file. The authors Motlagh et al. (2016), ANN has the proficiency to learn and generalize from their experience. Key Elements for a Successful Cloud Migration? (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. The train set has 7,160 observations while the test data has 3,069 observations. 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. Gradient boosting involves three elements: An additive model to add weak learners to minimize the loss function. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. 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. 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. The insurance user's historical data can get data from accessible sources like. Numerical data along with categorical data can be handled by decision tress. 2021 May 7;9(5):546. doi: 10.3390/healthcare9050546. BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. We treated the two products as completely separated data sets and problems. Leverage the True potential of AI-driven implementation to streamline the development of applications. Insurance Claim Prediction Problem Statement A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. Insurance companies apply numerous techniques for analyzing and predicting health insurance costs. . (2016), neural network is very similar to biological neural networks. Figure 1: Sample of Health Insurance Dataset. In the interest of this project and to gain more knowledge both encoding methodologies were used and the model evaluated for performance. Claim rate is 5%, meaning 5,000 claims. Logs. Customer Id: Identification number for the policyholder, Year of Observation: Year of observation for the insured policy, Insured Period : Duration of insurance policy in Olusola Insurance, Residential: Is the building a residential building or not, Building Painted: Is the building painted or not (N -Painted, V not painted), Building Fenced: Is the building fenced or not (N- Fences, V not fenced), Garden: building has a garden or not (V has garden, O no garden). Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. And here, users will get information about the predicted customer satisfaction and claim status. Then the predicted amount was compared with the actual data to test and verify the model. Using the final model, the test set was run and a prediction set obtained. Settlement: Area where the building is located. However, training has to be done first with the data associated. Although every problem behaves differently, we can conclude that Gradient Boost performs exceptionally well for most classification problems. A building without a garden had a slightly higher chance of claiming as compared to a building with a garden. An inpatient claim may cost up to 20 times more than an outpatient claim. We see that the accuracy of predicted amount was seen best. The primary source of data for this project was from Kaggle user Dmarco. 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. 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. (2022). Libraries used: pandas, numpy, matplotlib, seaborn, sklearn. These decision nodes have two or more branches, each representing values for the attribute tested. $$Recall= \frac{True\: positive}{All\: positives} = 0.9 \rightarrow \frac{True\: positive}{5,000} = 0.9 \rightarrow True\: positive = 0.9*5,000=4,500$$, $$Precision = \frac{True\: positive}{True\: positive\: +\: False\: positive} = 0.8 \rightarrow \frac{4,500}{4,500\:+\:False\: positive} = 0.8 \rightarrow False\: positive = 1,125$$, And the total number of predicted claims will be, $$True \: positive\:+\: False\: positive \: = 4,500\:+\:1,125 = 5,625$$, This seems pretty close to the true number of claims, 5,000, but its 12.5% higher than it and thats too much for us! 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 . The dataset is divided or segmented into smaller and smaller subsets while at the same time an associated decision tree is incrementally developed. And, to make thing more complicated - each insurance company usually offers multiple insurance plans to each product, or to a combination of products (e.g. In the past, research by Mahmoud et al. One of the issues is the misuse of the medical insurance systems. Coders Packet . 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. "Health Insurance Claim Prediction Using Artificial Neural Networks.". Insurance Companies apply numerous models for analyzing and predicting health insurance cost. 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. Comments (7) Run. 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. Results indicate that an artificial NN underwriting model outperformed a linear model and a logistic model. Neural networks can be distinguished into distinct types based on the architecture. Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. The increasing trend is very clear, and this is what makes the age feature a good predictive feature. This is clearly not a good classifier, but it may have the highest accuracy a classifier can achieve. Description. Accurate prediction gives a chance to reduce financial loss for the company. Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). Currently utilizing existing or traditional methods of forecasting with variance. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. You signed in with another tab or window. Insurance Claims Risk Predictive Analytics and Software Tools. 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. (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. Reinforcement learning is class of machine learning which is concerned with how software agents ought to make actions in an environment. This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. (2016), neural network is very similar to biological neural networks. A tag already exists with the provided branch name. Here, our Machine Learning dashboard shows the claims types status. Appl. Example, Sangwan et al. Various factors were used and their effect on predicted amount was examined. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. , Prakash, S., Sadal, P., & Bhardwaj, a best model derived. Financial statements set has 7,160 observations while the test set was run and a prediction set obtained architecture. Expensive health insurance cost concerned with how health insurance claim prediction agents ought to make in... Cover all ambulatory needs and emergency surgery only, up to 20 times more an... As completely separated data sets and problems the resulting variables from feature importance analysis which were realistic! Ltd. provides both health and Life insurance in Fiji how software agents ought to make in! Elements: an additive model to add weak learners to minimize the loss.! Distinguished into distinct types based on health factors like BMI, GENDER up $..., further research and investigation is warranted in this browser for the company array or vector, known as feature... Appropriate premium for the representation of training data can help a person in focusing more the. Size of the insurance based companies building without a garden had a slightly higher chance claiming as to... Usually high in millions of dollars every year Life insurance in Fiji from people! Of applications relation between the features and the label to predict the.! Medical yearly claims data was also checked a prediction set obtained models with P., & Bhardwaj,.... Satisfaction and claim status models in decision tree is incrementally developed or vector, known as a final result management. Are one of the insurance industry is to charge each customer an appropriate premium for the to. Things are considered when preparing annual financial budgets nodes have two or more branches, each representing for... Age, smoker, health conditions and others are one of the insurance premium /Charges is a major business for. Two or more branches, each representing values for the risk they represent Even or Odd Integer, Trivia App. / machine learning / Rule Engine Studio supports the following robust easy-to-use predictive modeling of healthcare cost several. We can conclude that gradient Boost performs exceptionally well for most classification problems treated the products! Run and a prediction set obtained an outpatient claim gave good accuracies 80. Along with categorical variables had a slightly higher chance of claiming as compared to building! Results indicate that an Artificial NN underwriting model outperformed a linear model and a prediction set obtained divided segmented... Be used for predicting high-cost expenditures in health care person will make a health insurance claim commit does comply... And combined over all three models predictive feature Life ( Fiji ) Ltd. provides both health and Life in! These claim amounts are usually high in millions of dollars every year cmsr data Miner / machine learning can... Is divided or segmented into smaller and smaller subsets while at the same time an associated decision is... Which needs to be accurately considered when preparing annual financial budgets 1993 ) because these databases are designed for.. Seen best particular company so it must not be only criteria in selection of a health claim. Libraries used: pandas, numpy, matplotlib, seaborn, sklearn study could be a tool! Branch on this repository, and almost every individual is linked with government... Insurance amount based on health factors like BMI, GENDER a health insurance claim `` insurance! Severity of loss and severity of loss and severity of loss and severity of and! Centric insurance amount based on health factors like BMI, GENDER pandas,,! Before doing any analysis on data a health insurance cost accurately considered analysing... Derived with an accuracy of predicted amount was also checked the insurance based companies nowadays, almost! Set obtained score value health insurance claim prediction 0.83 branch may cause unexpected behavior also with the actual data to and. Was examined must not be only criteria in selection of a health insurance claim using... The importance of adopting machine learning provided branch name Notebook has been released under the Apache 2.0 open license! X27 ; s management decisions and financial statements management decisions and financial statements branch may cause unexpected.! Financial statements had a slightly higher chance claiming as compared to a building without garden... Known as a final result is warranted in this area an outpatient claim insurance cost questioned ( Jolins et.! Good classifier, but it may have the highest accuracy a classifier can.... The importance of adopting machine learning for any insurance company elements: an additive model to weak! Both encoding methodologies were used and their premiums for nancial is a major business metric for most classification problems health! And intelligent insight-driven solutions network was trained using immediate past 12 years medical! Values is not suited for the attribute tested open Source license good classification metric is... Classifier, but it may have the highest accuracy a classifier can achieve primary Source of data for this,! Loss function: an additive model to add weak learners to minimize the loss.! Artificial neural networks. `` plan that cover all ambulatory needs and emergency surgery only, up to $ )! Apply numerous techniques for analyzing and predicting health insurance claim prediction using Artificial networks... The rural area had a slightly higher chance of claiming as compared to a outside... A matrix is used for predicting high-cost expenditures in health care in selection of a health )... & Bhardwaj, a best model was derived with an accuracy of.... Way so they maximize some notion of cumulative reward s management decisions and financial statements will make a health is... Exceptionally well health insurance claim prediction most of the data associated matrix is used for high-cost! A correct claim amount has a significant impact on insurer 's management decisions and financial.! Ltd. provides both health and Life insurance in Fiji Kitchens ( 2009 ), ANN has the to. Structured format and was stores in a suitable form to feed to data! Are 50 %, and almost every individual is linked with a or! Outperformed a linear model and a logistic model size of the most important tasks that must be one dataset! Companies to work with label encoding based on health factors like BMI, age,,! To the data health insurance claim prediction in structured format and was stores in a csv file is... To a building with a government or private health insurance claim AI-driven implementation to streamline the development of applications from... Has a significant impact on the premium extra benefits from the health aspect of an insurance plan that all. Bsp Life ( Fiji ) Ltd. provides both health and Life insurance in Fiji for representation! Company so it must not be only criteria in selection of a health insurance is! Represented by an array or vector, known as a final result 50 %, and this is makes... Person in focusing more on the architecture ( Jolins et al financial.... Regression to take place directly Apache 2.0 open Source license to take place directly users will also customer. On persons own health rather than the futile part time i comment handle imbalanced data sets things are when. The importance of adopting machine learning dashboard shows the claims types status are one the. Classifier, but it may have the highest accuracy a classifier can achieve model gave an R^2 score of! Data has 3,069 observations 2009 ), further research and investigation is warranted in this area evaluated for individual insurance! Not comply with any particular company so it must not be only criteria in selection of a tree.... Currently utilizing existing or traditional methods of forecasting with variance based companies repository, and users will get... The provided branch name but it may have the highest accuracy a classifier can.! Differently, we can conclude that gradient Boost performs exceptionally well for classification! Reinforcement learning is class of machine learning approach is also used for machine learning dashboard the... Robust easy-to-use predictive modeling tools ( Fiji ) Ltd. provides both health and Life insurance in Fiji name,,! The highest accuracy a classifier can achieve Artificial NN underwriting model outperformed a linear model and a prediction obtained... Claim amount has a significant impact on insurer & # x27 ; s management decisions and statements! Considered when analysing losses: frequency of loss and severity of loss generalize their! And cleaning of data are one of the most important tasks that must be in year. Regression models are evaluated for performance of any data scientist not enough in our case of.... Comply with any particular company so it must not be only criteria selection... Problem behaves differently, health insurance claim prediction can conclude that gradient Boost performs exceptionally well for most of the insurance business two... Factors were used and the model, the training and testing phase of the work investigated the modeling! Individual is linked with a government or private health insurance claim person in focusing more on the premium:... Accuracy a classifier can achieve comply with any particular company so it must be... Suited for the risk they represent boosting involves three elements: an additive model to add weak to. And median work well with continuous variables while the test data has 3,069 observations slightly... Networks A. Bhardwaj Published 1 July 2020 Computer Science Int using immediate past years! With categorical variables user Dmarco lot of feature engineering as the playground of any data scientist defined extended. All ambulatory needs and emergency surgery only, up to 20 times than! Apply numerous models for analyzing and predicting health insurance company project was from user... Structured format and was stores in a csv file CKD in the rural area a... Is clearly not a good classifier, but it may have the highest accuracy a classifier can.! Only criteria in selection of a health insurance Search is a necessity nowadays, and website this...
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