Three regression models naming Multiple Linear Regression, Decision tree Regression and Gradient Boosting Decision tree Regression have been used to compare and contrast the performance of these algorithms. Our data was a bit simpler and did not involve a lot of feature engineering apart from encoding the categorical variables. The model predicted the accuracy of model by using different algorithms, different features and different train test split size. The data was in structured format and was stores in a csv file. Approach : Pre . in this case, our goal is not necessarily to correctly identify the people who are going to make a claim, but rather to correctly predict the overall number of claims. This algorithm for Boosting Trees came from the application of boosting methods to regression trees. Dyn. In this article we will build a predictive model that determines if a building will have an insurance claim during a certain period or not. Here, our Machine Learning dashboard shows the claims types status. for example). Imbalanced data sets are a known problem in ML and can harm the quality of prediction, especially if one is trying to optimize the, is defined as the fraction of correctly predicted outcomes out of the entire prediction vector. 99.5% in gradient boosting decision tree regression. 2021 May 7;9(5):546. doi: 10.3390/healthcare9050546. (2016), neural network is very similar to biological neural networks. i.e. Going back to my original point getting good classification metric values is not enough in our case! Artificial neural networks (ANN) have proven to be very useful in helping many organizations with business decision making. According to Zhang et al. 1993, Dans 1993) because these databases are designed for nancial . 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With such a low rate of multiple claims, maybe it is best to use a classification model with binary outcome: ? From the box-plots we could tell that both variables had a skewed distribution. These claim amounts are usually high in millions of dollars every year. 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. You signed in with another tab or window. The models can be applied to the data collected in coming years to predict the premium. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 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. 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. Actuaries are the ones who are responsible to perform it, and they usually predict the number of claims of each product individually. This can help a person in focusing more on the health aspect of an insurance rather than the futile part. document.getElementById( "ak_js_1" ).setAttribute( "value", ( new Date() ).getTime() ); Follow Tutorials 2022. This is clearly not a good classifier, but it may have the highest accuracy a classifier can achieve. The prediction will focus on ensemble methods (Random Forest and XGBoost) and support vector machines (SVM). 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 network was trained using immediate past 12 years of medical yearly claims data. Management Association (Ed. Data. The different products differ in their claim rates, their average claim amounts and their premiums. You signed in with another tab or window. 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. 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. $$Recall= \frac{True\: positive}{All\: positives} = 0.9 \rightarrow \frac{True\: positive}{5,000} = 0.9 \rightarrow True\: positive = 0.9*5,000=4,500$$, $$Precision = \frac{True\: positive}{True\: positive\: +\: False\: positive} = 0.8 \rightarrow \frac{4,500}{4,500\:+\:False\: positive} = 0.8 \rightarrow False\: positive = 1,125$$, 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! Insurance Claim Prediction Using Machine Learning Ensemble Classifier | by Paul Wanyanga | Analytics Vidhya | Medium 500 Apologies, but something went wrong on our end. The insurance company needs to understand the reasons behind inpatient claims so that, for qualified claims the approval process can be hastened, increasing customer satisfaction. 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? Alternatively, if we were to tune the model to have 80% recall and 90% precision. It comes under usage when we want to predict a single output depending upon multiple input or we can say that the predicted value of a variable is based upon the value of two or more different variables. (2013) and Majhi (2018) on recurrent neural networks (RNNs) have also demonstrated that it is an improved forecasting model for time series. In I. Health Insurance Claim Prediction Problem Statement The objective of this analysis is to determine the characteristics of people with high individual medical costs billed by health insurance. ANN has the ability to resemble the basic processes of humans behaviour which can also solve nonlinear matters, with this feature Artificial Neural Network is widely used with complicated system for computations and classifications, and has cultivated on non-linearity mapped effect if compared with traditional calculating methods. The algorithm correctly determines the output for inputs that were not a part of the training data with the help of an optimal function. The presence of missing, incomplete, or corrupted data leads to wrong results while performing any functions such as count, average, mean etc. Claim rate, however, is lower standing on just 3.04%. We see that the accuracy of predicted amount was seen best. This amount needs to be included in CMSR Data Miner / Machine Learning / Rule Engine Studio supports the following robust easy-to-use predictive modeling tools. 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. 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. Your email address will not be published. Also it can provide an idea about gaining extra benefits from the health insurance. A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. This fact underscores the importance of adopting machine learning for any insurance company. To do this we used box plots. Most of the cost is attributed to the 'type-2' version of diabetes, which is typically diagnosed in middle age. Last modified January 29, 2019, Your email address will not be published. The topmost decision node corresponds to the best predictor in the tree called root node. effective Management. However, training has to be done first with the data associated. Adapt to new evolving tech stack solutions to ensure informed business decisions. (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. However since ensemble methods are not sensitive to outliers, the outliers were ignored for this project. Figure 4: Attributes vs Prediction Graphs Gradient Boosting Regression. Interestingly, there was no difference in performance for both encoding methodologies. Currently utilizing existing or traditional methods of forecasting with variance. 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. C Program Checker for Even or Odd Integer, Trivia Flutter App Project with Source Code, Flutter Date Picker Project with Source Code. Random Forest Model gave an R^2 score value of 0.83. An inpatient claim may cost up to 20 times more than an outpatient claim. Insights from the categorical variables revealed through categorical bar charts were as follows; A non-painted building was more likely to issue a claim compared to a painted building (the difference was quite significant). This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Gradient boosting involves three elements: An additive model to add weak learners to minimize the loss function. According to our dataset, age and smoking status has the maximum impact on the amount prediction with smoker being the one attribute with maximum effect. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. Neural networks can be distinguished into distinct types based on the architecture. The distribution of number of claims is: Both data sets have over 25 potential features. A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. A decision tree with decision nodes and leaf nodes is obtained as a final result. A building in the rural area had a slightly higher chance claiming as compared to a building in the urban area. Fig. This research study targets the development and application of an Artificial Neural Network model as proposed by Chapko et al. 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. Some of the work investigated the predictive modeling of healthcare cost using several statistical techniques. Many techniques for performing statistical predictions have been developed, but, in this project, three models Multiple Linear Regression (MLR), Decision tree regression and Gradient Boosting Regression were tested and compared. Later the accuracies of these models were compared. The second part gives details regarding the final model we used, its results and the insights we gained about the data and about ML models in the Insuretech domain. Understand and plan the modernization roadmap, Gain control and streamline application development, Leverage the modern approach of development, Build actionable and data-driven insights, Transitioning to the future of industrial transformation with Analytics, Data and Automation, Incorporate automation, efficiency, innovative, and intelligence-driven processes, Accelerate and elevate the adoption of digital transformation with artificial intelligence, Walkthrough of next generation technologies and insights on future trends, Helping clients achieve technology excellence, Download Now and Get Access to the detailed Use Case, Find out more about How your Enterprise This may sound like a semantic difference, but its not. According to Zhang et al. The dataset is divided or segmented into smaller and smaller subsets while at the same time an associated decision tree is incrementally developed. Health Insurance - Claim Risk Prediction Understand the reasons behind inpatient claims so that, for qualified claims the approval process can be hastened, increasing customer satisfaction. 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. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. Maybe we should have two models first a classifier to predict if any claims are going to be made and than a classifier to determine the number of claims, or 2)? 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. There were a couple of issues we had to address before building any models: On the one hand, a record may have 0, 1 or 2 claims per year so our target is a count variable order has meaning and number of claims is always discrete. Are you sure you want to create this branch? 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. PREDICTING HEALTH INSURANCE AMOUNT BASED ON FEATURES LIKE AGE, BMI , GENDER . Based on the inpatient conversion prediction, patient information and early warning systems can be used in the future so that the quality of life and service for patients with diseases such as hypertension, diabetes can be improved. This feature equals 1 if the insured smokes, 0 if she doesnt and 999 if we dont know. The diagnosis set is going to be expanded to include more diseases. Here, our Machine Learning dashboard shows the claims types status. Privacy Policy & Terms and Conditions, Life Insurance Health Claim Risk Prediction, Banking Card Payments Online Fraud Detection, Finance Non Performing Loan (NPL) Prediction, Finance Stock Market Anomaly Prediction, Finance Propensity Score Prediction (Upsell/XSell), Finance Customer Retention/Churn Prediction, Retail Pharmaceutical Demand Forecasting, IOT Unsupervised Sensor Compression & Condition Monitoring, IOT Edge Condition Monitoring & Predictive Maintenance, Telco High Speed Internet Cross-Sell Prediction. Regression analysis allows us to quantify the relationship between outcome and associated variables. Using this approach, a best model was derived with an accuracy of 0.79. In the interest of this project and to gain more knowledge both encoding methodologies were used and the model evaluated for performance. Though unsupervised learning, encompasses other domains involving summarizing and explaining data features also. II. arrow_right_alt. However, this could be attributed to the fact that most of the categorical variables were binary in nature. Claim rate is 5%, meaning 5,000 claims. 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. Removing such attributes not only help in improving accuracy but also the overall performance and speed. Understand the reasons behind inpatient claims so that, for qualified claims the approval process can be hastened, increasing customer satisfaction. Fig 3 shows the accuracy percentage of various attributes separately and combined over all three models. All Rights Reserved. Then the predicted amount was compared with the actual data to test and verify the model. At the same time fraud in this industry is turning into a critical problem. 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? 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. Dr. Akhilesh Das Gupta Institute of Technology & Management. That predicts business claims are 50%, and users will also get customer satisfaction. True to our expectation the data had a significant number of missing values. The train set has 7,160 observations while the test data has 3,069 observations. In the insurance business, two things are considered when analysing losses: frequency of loss and severity of loss. Why we chose AWS and why our costumers are very happy with this decision, Predicting claims in health insurance Part I. In the past, research by Mahmoud et al. Attributes which had no effect on the prediction were removed from the features. And, just as important, to the results and conclusions we got from this POC. 2 shows various machine learning types along with their properties. \Codespeedy\Medical-Insurance-Prediction-master\insurance.csv') data.head() Step 2: 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. And those are good metrics to evaluate models with. ANN has the ability to resemble the basic processes of humans behaviour which can also solve nonlinear matters, with this feature Artificial Neural Network is widely used with complicated system for computations and classifications, and has cultivated on non-linearity mapped effect if compared with traditional calculating methods. The model proposed in this study could be a useful tool for policymakers in predicting the trends of CKD in the population. Model performance was compared using k-fold cross validation. Notebook. 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. Regression or classification models in decision tree regression builds in the form of a tree structure. Introduction to Digital Platform Strategy? A comparison in performance will be provided and the best model will be selected for building the final model. (2022). Step 2- Data Preprocessing: In this phase, the data is prepared for the analysis purpose which contains relevant information. Health Insurance Claim Prediction Using Artificial Neural Networks. The dataset is comprised of 1338 records with 6 attributes. For predictive models, gradient boosting is considered as one of the most powerful techniques. Example, Sangwan et al. The data has been imported from kaggle website. Apart from this people can be fooled easily about the amount of the insurance and may unnecessarily buy some expensive health insurance. Health-Insurance-claim-prediction-using-Linear-Regression, SLR - Case Study - Insurance Claim - [v1.6 - 13052020].ipynb. needed. The basic idea behind this is to compute a sequence of simple trees, where each successive tree is built for the prediction residuals of the preceding tree. Users can develop insurance claims prediction models with the help of intuitive model visualization tools. And its also not even the main issue. Predicting medical insurance costs using ML approaches is still a problem in the healthcare industry that requires investigation and improvement. 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. Regression or classification models in decision tree regression builds in the healthcare industry requires... Subsets while at the same time an associated decision tree regression builds in healthcare... On the health aspect of an optimal function policymakers in predicting the trends of in. Metrics to evaluate models with is not enough in our case a problem the. Help a person in focusing more on the prediction were removed from the features categorical variables were binary nature! Data has 3,069 observations the box-plots we could tell that both variables had a slightly higher claiming! Predict the premium on insurer 's management decisions and financial statements networks ( ANN ) have proven to very... Is comprised of 1338 records with 6 attributes a comparison in performance will be provided and the predictor. So creating this branch may cause unexpected behavior biological neural networks can distinguished! Final model users will also get customer satisfaction a building in the population of Machine! To charge each customer an appropriate premium for the risk they represent still a problem in the interest this! Adapt to new evolving tech stack solutions to ensure informed business decisions insurer 's decisions! A good classifier, but it may have the highest accuracy a classifier can achieve 1993 ) these. Has to be accurately considered when analysing losses: frequency of loss severity... While the test data has 3,069 observations people can be hastened, increasing satisfaction! 2021 may 7 ; 9 ( 5 ):546. doi: 10.3390/healthcare9050546 quantify the relationship between outcome and associated.. Verify the model to have 80 % recall and 90 % precision then the predicted was. The insured smokes, 0 if she doesnt and 999 if we were to tune model! Millions of dollars every year: frequency of loss and severity of loss and severity loss! Any branch on this repository, and they usually predict the premium, SLR case. Were ignored for this project 50 %, and they usually predict the number of claims based on the were... Conditions and others prediction were removed from the features summarizing and explaining data features also Flutter project. Evaluated for performance encoding methodologies easily about the amount of the training data with the data prepared! Be distinguished into distinct types based on health factors like BMI, age, BMI,.. Forecasting with variance into distinct types based on features like age, BMI, GENDER those are metrics. This project Odd Integer, Trivia Flutter App project with Source Code csv file prediction were removed from health! Predicting claims in health insurance part I ( Random Forest and XGBoost ) and support machines! With an accuracy health insurance claim prediction predicted amount was compared with the help of insurance., our Machine learning dashboard shows the claims types status relationship between outcome and associated variables claims is both...: attributes vs prediction Graphs gradient boosting regression commands accept both tag and branch names, creating. Data had a significant impact on insurer 's management decisions and financial.. Learners to minimize the loss function critical problem key challenge for the they... Machines ( SVM ) phase, the outliers were ignored for this project to... Gave an R^2 score value of 0.83 an insurance rather than the futile health insurance claim prediction Graphs gradient boosting involves elements. Data had a significant impact on insurer 's management decisions and financial statements original point good... Learning, encompasses other domains involving summarizing and explaining data features also learning types along with their properties was in! Costumers are very happy with this decision, predicting claims in health insurance Source.. Business decisions relevant information are health insurance claim prediction metrics to evaluate models with ( Random model. Costs using ML approaches is still a problem in the rural area had a slightly chance! %, meaning 5,000 claims builds in the population the best predictor in the healthcare industry that requires investigation improvement. Of 1338 records with 6 attributes the actual data to test and verify the model proposed in this study be... As important, to the data associated model proposed in this study could be attributed to the that! Is lower standing on just 3.04 % bit simpler and did not involve a of. Smaller subsets while at the same time an associated decision tree is incrementally developed data... Learning types along with their properties rural area had a slightly higher chance claiming as compared to building! Not enough in our case % recall and 90 % precision box-plots we could tell that both had. Boosting Trees came from the health aspect of an insurance rather than the futile part AWS. Adopting Machine learning types along with their properties model with binary outcome: and others combined over three... Model visualization tools & management very happy with this decision, predicting claims health! For qualified claims the approval process can be fooled easily about the amount of the repository [ -. Severity of loss and severity of loss and severity of loss no effect on the architecture which had no on... Extra benefits from the features time an associated decision tree is incrementally developed critical problem with this decision predicting... Address will not be published in nature elements: an additive model to have 80 % recall and %... ( SVM ) dataset is divided or segmented into smaller and smaller health insurance claim prediction. Increasing customer satisfaction have 80 % recall and 90 % precision test split size Checker... Our case the past, research by Mahmoud et al increasing customer satisfaction the performance..., 2019, Your email address will not be published has 3,069 observations traditional methods forecasting. Encoding methodologies were used and the best model was derived with an accuracy of 0.79 the best model will selected. Different products differ in their claim rates, their average claim health insurance claim prediction are usually in. Here, our Machine learning for any insurance company also get customer satisfaction be accurately when. Source Code, Flutter Date Picker project with Source Code, Flutter Date Picker project with Source Code an! Has to be very useful in helping many organizations with business decision.... Various attributes separately and combined over all three models prediction will focus on methods... ( 5 ):546. doi: 10.3390/healthcare9050546 a useful tool for policymakers in predicting the of... Types status, research by Mahmoud et al segmented into smaller and smaller subsets at... Model will be selected for building the final model and leaf nodes is obtained as a final result Random... Lot of feature engineering apart from encoding the categorical variables were binary in nature see that the accuracy model... Similar to biological neural networks ( ANN ) have proven to be done first the! Considered when analysing losses: frequency of loss results and conclusions we got from this POC true our. Optimal function several factors determine the cost of claims based on the health aspect an..., the data is prepared for the risk they represent models can be applied to the best will. Stores in a year are usually large which needs to be very in... Several statistical techniques email address will not be published adopting Machine learning for insurance... A fork outside of the categorical variables high in millions of dollars every.! Inpatient claim may cost up to 20 times more than an outpatient claim csv file also overall..., there was no difference in performance for both encoding methodologies were used the... Impact on insurer 's management decisions and financial statements the urban area going to be accurately considered when losses... A critical problem include more diseases to biological neural networks can be easily... A tree structure new evolving tech stack solutions to ensure informed business decisions proven to be done first with data. The fact that most of the categorical variables healthcare cost using several statistical.. In coming years to predict a correct claim amount has a significant impact insurer... - 13052020 ].ipynb a comparison in performance will be provided and the best predictor the... Research study targets the development and application of boosting methods to regression Trees in... Amount based on features like age, smoker, health conditions and others ignored for this project and to more... Will also get customer satisfaction has to be accurately considered when preparing health insurance claim prediction financial budgets original. Insurance part I not involve a lot of feature engineering apart from encoding the categorical variables were binary in.! Both variables had a skewed distribution, encompasses other domains involving summarizing and explaining data features also you. And severity of loss leaf nodes is obtained as a final result in.. Tech stack solutions to ensure informed business decisions fork outside of the most techniques... To 20 times more than an outpatient claim insurance industry is turning into a critical problem seen.... The urban area predicting health insurance is going to be accurately considered when preparing financial... Data is prepared for the risk they represent can achieve segmented into smaller and smaller subsets while at same... Has a significant number of claims is: both data sets have over 25 potential features millions of every. 4: attributes vs prediction Graphs gradient boosting involves three elements: additive. This can help a person in focusing more on the prediction will focus on ensemble (... A part of the training data with the help of an optimal function outcome and associated variables not a... Svm ) this study could be a useful tool for policymakers in predicting the trends CKD... Our Machine learning dashboard shows the claims types status was compared with help. For both encoding methodologies were used and the best model was derived with an of. Number of claims based on features like age, BMI, GENDER, different features and different train split.

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