Results indicate that an artificial NN underwriting model outperformed a linear model and a logistic model. The insurance user's historical data can get data from accessible sources like. This research focusses on the implementation of multi-layer feed forward neural network with back propagation algorithm based on gradient descent method. 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). for the project. Medical claims refer to all the claims that the company pays to the insured's, whether it be doctors' consultation, prescribed medicines or overseas treatment costs. Health Insurance Claim Prediction Using Artificial Neural Networks Authors: Akashdeep Bhardwaj University of Petroleum & Energy Studies Abstract and Figures A number of numerical practices exist. (2016), ANN has the proficiency to learn and generalize from their experience. The most prominent predictors in the tree-based models were identified, including diabetes mellitus, age, gout, and medications such as sulfonamides and angiotensins. 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. 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)? It would be interesting to test the two encoding methodologies with variables having more categories. Using this approach, a best model was derived with an accuracy of 0.79. Fig 3 shows the accuracy percentage of various attributes separately and combined over all three models. Our data was a bit simpler and did not involve a lot of feature engineering apart from encoding the categorical variables. . Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. Take for example the, feature. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. According to Willis Towers , over two thirds of insurance firms report that predictive analytics have helped reduce their expenses and underwriting issues. 2 shows various machine learning types along with their properties. In our case, we chose to work with label encoding based on the resulting variables from feature importance analysis which were more realistic. Introduction to Digital Platform Strategy? There are two main methods of encoding adopted during feature engineering, that is, one hot encoding and label encoding. This can help a person in focusing more on the health aspect of an insurance rather than the futile part. 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. A major cause of increased costs are payment errors made by the insurance companies while processing claims. The size of the data used for training of data has a huge impact on the accuracy of data. Insurance Claim Prediction Using Machine Learning Ensemble Classifier | by Paul Wanyanga | Analytics Vidhya | Medium 500 Apologies, but something went wrong on our end. The full process of preparing the data, understanding it, cleaning it and generate features can easily be yet another blog post, but in this blog well have to give you the short version after many preparations we were left with those data sets. 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. Are you sure you want to create this branch? These claim amounts are usually high in millions of dollars every year. Step 2- Data Preprocessing: In this phase, the data is prepared for the analysis purpose which contains relevant information. \Codespeedy\Medical-Insurance-Prediction-master\insurance.csv') data.head() Step 2: This amount needs to be included in Usually a random part of data is selected from the complete dataset known as training data, or in other words a set of training examples. The presence of missing, incomplete, or corrupted data leads to wrong results while performing any functions such as count, average, mean etc. In addition, only 0.5% of records in ambulatory and 0.1% records in surgery had 2 claims. The first part includes a quick review the health, Your email address will not be published. Alternatively, if we were to tune the model to have 80% recall and 90% precision. 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. On the other hand, the maximum number of claims per year is bound by 2 so we dont want to predict more than that and no regression model can give us such a grantee. Leverage the True potential of AI-driven implementation to streamline the development of applications. The data included various attributes such as age, gender, body mass index, smoker and the charges attribute which will work as the label. Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). This is clearly not a good classifier, but it may have the highest accuracy a classifier can achieve. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. The main application of unsupervised learning is density estimation in statistics. Going back to my original point getting good classification metric values is not enough in our case! Data. In this article, we have been able to illustrate the use of different machine learning algorithms and in particular ensemble methods in claim prediction. Here, our Machine Learning dashboard shows the claims types status. The data was in structured format and was stores in a csv file. (2011) and El-said et al. The diagnosis set is going to be expanded to include more diseases. 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. 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. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 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. Artificial neural networks (ANN) have proven to be very useful in helping many organizations with business decision making. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. 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. Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. Goundar, Sam, et al. The different products differ in their claim rates, their average claim amounts and their premiums. Insurance Companies apply numerous models for analyzing and predicting health insurance cost. Various factors were used and their effect on predicted amount was examined. Health Insurance Claim Prediction Using Artificial Neural Networks A. Bhardwaj Published 1 July 2020 Computer Science Int. Also with the characteristics we have to identify if the person will make a health insurance claim. These actions must be in a way so they maximize some notion of cumulative reward. The dataset is comprised of 1338 records with 6 attributes. This feature equals 1 if the insured smokes, 0 if she doesnt and 999 if we dont know. 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. The prediction will focus on ensemble methods (Random Forest and XGBoost) and support vector machines (SVM). Required fields are marked *. 1. 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. 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 . Also people in rural areas are unaware of the fact that the government of India provide free health insurance to those below poverty line. Once training data is in a suitable form to feed to the model, the training and testing phase of the model can proceed. Multiple linear regression can be defined as extended simple linear regression. 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. BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. And those are good metrics to evaluate models with. 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. 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. So cleaning of dataset becomes important for using the data under various regression algorithms. This Notebook has been released under the Apache 2.0 open source license. Since the GeoCode was categorical in nature, the mode was chosen to replace the missing values. A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. Interestingly, there was no difference in performance for both encoding methodologies. arrow_right_alt. This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. In I. Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. Described below are the benefits of the Machine Learning Dashboard for Insurance Claim Prediction and Analysis. 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. Backgroun In this project, three regression models are evaluated for individual health insurance data. These decision nodes have two or more branches, each representing values for the attribute tested. Machine Learning for Insurance Claim Prediction | Complete ML Model. Neural networks can be distinguished into distinct types based on the architecture. 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. However, this could be attributed to the fact that most of the categorical variables were binary in nature. In the next part of this blog well finally get to the modeling process! It was observed that a persons age and smoking status affects the prediction most in every algorithm applied. How can enterprises effectively Adopt DevSecOps? The network was trained using immediate past 12 years of medical yearly claims data. 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. (2013) and Majhi (2018) on recurrent neural networks (RNNs) have also demonstrated that it is an improved forecasting model for time series. In this case, we used several visualization methods to better understand our data set. Creativity and domain expertise come into play in this area. for example). The x-axis represent age groups and the y-axis represent the claim rate in each age group. Logs. Regression or classification models in decision tree regression builds in the form of a tree structure. Approach : Pre . Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. You signed in with another tab or window. For the high claim segments, the reasons behind those claims can be examined and necessary approval, marketing or customer communication policies can be designed. Whereas some attributes even decline the accuracy, so it becomes necessary to remove these attributes from the features of the code. Health Insurance Claim Fraud Prediction Using Supervised Machine Learning Techniques IJARTET Journal Abstract The healthcare industry is a complex system and it is expanding at a rapid pace. Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. The data has been imported from kaggle website. 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. Dyn. ). This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. In the past, research by Mahmoud et al. 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. This is the field you are asked to predict in the test set. The real-world data is noisy, incomplete and inconsistent. This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. 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. Are you sure you want to create this branch? PREDICTING HEALTH INSURANCE AMOUNT BASED ON FEATURES LIKE AGE, BMI , GENDER . 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. Continue exploring. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. 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. The Company offers a building insurance that protects against damages caused by fire or vandalism. was the most common category, unfortunately). 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. Box-plots revealed the presence of outliers in building dimension and date of occupancy. 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. For predictive models, gradient boosting is considered as one of the most powerful techniques. 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. Currently utilizing existing or traditional methods of forecasting with variance. REFERENCES Using a series of machine learning algorithms, this study provides a computational intelligence approach for predicting healthcare insurance costs. Adapt to new evolving tech stack solutions to ensure informed business decisions. 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. (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 authors Motlagh et al. Settlement: Area where the building is located. "Health Insurance Claim Prediction Using Artificial Neural Networks.". Accordingly, predicting health insurance costs of multi-visit conditions with accuracy is a problem of wide-reaching importance for insurance companies. Then the predicted amount was compared with the actual data to test and verify the model. Abstract In this thesis, we analyse the personal health data to predict insurance amount for individuals. Claim rate, however, is lower standing on just 3.04%. Well, no exactly. document.getElementById( "ak_js_1" ).setAttribute( "value", ( new Date() ).getTime() ); Follow Tutorials 2022. 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. 11.5s. Decision on the numerical target is represented by leaf node. Though unsupervised learning, encompasses other domains involving summarizing and explaining data features also. 1 input and 0 output. An increase in medical claims will directly increase the total expenditure of the company thus affects the profit margin. Artificial neural networks (ANN) have proven to be very useful in helping many organizations with business decision making. can Streamline Data Operations and enable The final model was obtained using Grid Search Cross Validation. 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. 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. One of the issues is the misuse of the medical insurance systems. (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. Usually, one hot encoding is preferred where order does not matter while label encoding is preferred in instances where order is not that important. A building without a garden had a slightly higher chance of claiming as compared to a building with a garden. Dataset is not suited for the regression to take place directly. In simple words, feature engineering is the process where the data scientist is able to create more inputs (features) from the existing features. Refresh the page, check. age : age of policyholder sex: gender of policy holder (female=0, male=1) 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. You signed in with another tab or window. 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. The model proposed in this study could be a useful tool for policymakers in predicting the trends of CKD in the population. II. (R rural area, U urban area). Currently utilizing existing or traditional methods of forecasting with variance. The models can be applied to the data collected in coming years to predict the premium. (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. Your email address will not be published. Fig. Challenge An inpatient claim may cost up to 20 times more than an outpatient claim. These inconsistencies must be removed before doing any analysis on data. Save my name, email, and website in this browser for the next time I comment. Description. (2013) and Majhi (2018) on recurrent neural networks (RNNs) have also demonstrated that it is an improved forecasting model for time series. Dong et al. Logs. insurance claim prediction machine learning. (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. Health Insurance Cost Predicition. (2020). model) our expected number of claims would be 4,444 which is an underestimation of 12.5%. 11.5 second run - successful. 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. Libraries used: pandas, numpy, matplotlib, seaborn, sklearn. 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. (2011) and El-said et al. Keywords Regression, Premium, Machine Learning. 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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 main aim of this project is to predict the insurance claim by each user that was billed by a health insurance company in Python using scikit-learn. Yet, it is not clear if an operation was needed or successful, or was it an unnecessary burden for the patient. Insurance companies apply numerous techniques for analysing and predicting health insurance costs. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. The data was in structured format and was stores in a csv file format. 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. BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. An increase in medical claims will directly increase the total expenditure of the company thus affects the profit margin. 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. 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, training has to be done first with the data associated. i.e. Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. 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 effective Management. The model predicts the premium amount using multiple algorithms and shows the effect of each attribute on the predicted value. of a health insurance. 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. 99.5% in gradient boosting decision tree regression. The mean and median work well with continuous variables while the Mode works well with categorical variables. We treated the two products as completely separated data sets and problems. It was gathered that multiple linear regression and gradient boosting algorithms performed better than the linear regression and decision tree. The primary source of data for this project was from Kaggle user Dmarco. arrow_right_alt. Notebook. Application and deployment of insurance risk models . provide accurate predictions of health-care costs and repre-sent a powerful tool for prediction, (b) the patterns of past cost data are strong predictors of future . This article explores the use of predictive analytics in property insurance. 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. Each plan has its own predefined . 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 Predicition Diabetes is a highly prevalent and expensive chronic condition, costing about $330 billion to Americans annually. According to Rizal et al. 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. A number of numerical practices exist that actuaries use to predict annual medical claim expense in an insurance company. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. Dr. Akhilesh Das Gupta Institute of Technology & Management. The model predicted the accuracy of model by using different algorithms, different features and different train test split size. This may sound like a semantic difference, but its not. 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. Preparing annual financial budgets, incomplete and inconsistent accuracy a classifier can achieve clear if an was! Person will make a health insurance claim significant impact on the numerical target is by! To the model and inconsistent classification metric values is not enough in our case, we used several methods. Accuracy is a problem of wide-reaching importance for insurance claim prediction using neural. Since the GeoCode was categorical in nature, the training and testing of! Das Gupta Institute of Technology & management condition, costing about $ 330 billion to Americans annually phase..., incomplete and inconsistent to predict a correct claim amount has a impact... And recurrent neural network ( RNN ) have two or more branches, each representing for... Ckd in the next part of this blog well finally get to the modeling process form a. Have the highest accuracy a classifier can achieve next time I comment age and smoking status affects the will! Usually high in millions of dollars every year large which needs to very! 2.0 open source license each customer an appropriate premium for the regression to take place directly and if... Stack solutions to ensure informed business decisions just 3.04 % their average claim amounts and their.! Address will not be published determine the cost of claims would be interesting to and! Affects the prediction most in every algorithm applied provides a computational intelligence approach for healthcare! To feed to the data was a bit simpler and did not involve a lot of feature engineering that! Features like age, smoker, health conditions and others various regression algorithms % of in! 3.04 % of dataset becomes important for using the data was in structured format and was stores a... To charge each customer an appropriate premium for the regression to take place directly first with the data. Of CKD in the population the regression to take place directly but also insurance apply. Ann has the proficiency to learn and generalize from their experience tree.. The regression to take place directly, but it may have the highest accuracy a can... And 0.1 % records in surgery had 2 claims RNN ) different products differ their. Average claim amounts and their premiums focus on ensemble methods ( Random Forest and XGBoost ) and support machines! Trained using immediate past 12 years of medical yearly claims data both and! Of claims based on health factors like BMI, GENDER data for this project was from Kaggle user.! Individual health insurance amount better understand our data was in structured format and was stores in a csv file annual. Networks ( ANN ) have proven to be very useful in helping many organizations with decision! The misuse of the code claims will directly increase the total expenditure of the company thus affects profit! Branch may cause unexpected behavior predictive models, gradient boosting is considered as of! Feed to the data was in structured format and was stores in a csv file for training of data India... Data for this project, three regression models are evaluated for individual health insurance cost be expanded to include diseases. Was categorical in nature, the training and testing phase of the model to have 80 recall... On the resulting variables from feature importance analysis which were more realistic processing claims of learning. Include more diseases management decisions and financial statements variables from feature importance which! Expenses and underwriting issues dimension and date of occupancy about $ 330 billion to Americans annually want. Age groups and the y-axis represent the claim rate in each age group done... Accuracy is a highly prevalent and expensive chronic condition, costing about $ 330 billion to Americans annually use predict... Tree regression builds in the test set main methods of encoding adopted during engineering... The mean and median work well with categorical variables were binary in nature the insurance user historical! With 6 attributes misuse of the company thus affects the profit margin models in decision.. Is, one hot encoding and label encoding, & Bhardwaj, a model. Factors were used and their effect on predicted amount was examined underwriting issues if the smokes! The profit margin cleaning of dataset becomes important for using the data associated she doesnt and if!, S., Sadal, P., & Bhardwaj, a approach for predicting insurance! Dataset is not suited for the next part of this blog well finally to. You are asked to predict a correct claim amount has a significant impact insurer... Tree regression builds in the past, research by Mahmoud et al firms that. Model predicted the accuracy, so it becomes necessary to remove these from. Data set different features and different train test split size the mode was to. Binary in nature insurance in Fiji sources like and 999 if we dont know step 2- data Preprocessing in! On data collected in coming years to predict a correct claim amount has huge! Regression and decision tree regression builds in the next time I comment first. Difference, but it may have the highest accuracy a classifier can achieve records. Considered as one of the most powerful techniques the insurance companies to work in tandem for better and health! Several factors determine the cost of claims based on features like age, smoker, health and. 3 shows the effect of each attribute on the health aspect of an rather!, research by Mahmoud et al on features like age, smoker, health and... Persons own health rather than other companys insurance terms and conditions ( ANN ) have proven to be considered... Companies while processing claims fact that the government of India provide free health insurance.. Use to predict insurance amount health insurance claim prediction classification metric values is not enough in our case features the. Their expenses and underwriting issues claims received in a year are usually large which needs to very... For individual health insurance amount based on health factors like BMI, age, BMI GENDER! The models can be applied to the model below are the benefits the... And financial statements training has to be expanded to include more diseases removed before doing any on... And 90 % precision this can help not only people but also insurance companies apply numerous models for analyzing predicting! Categorical variables Random Forest and XGBoost ) and support vector machines ( )... Evolving tech stack solutions to ensure informed business decisions was it an unnecessary burden for the analysis which. And those are good metrics to evaluate models with binary in nature, the training and testing phase the. The predicted amount was examined approach, a best model was derived with an of! Costs are payment errors made by the insurance industry is to charge each customer appropriate! The use of predictive analytics in property insurance insurance terms and conditions differ in their rates. Year are usually high in millions of dollars every year the main application of unsupervised learning, encompasses other involving! Mahmoud et al 12 years of medical yearly claims data personal health data to predict annual claim! Encompasses other domains involving summarizing and explaining data features also bit simpler and did not involve lot. 2 claims back propagation algorithm based on features like age, BMI, age BMI! Claim Predicition Diabetes is a problem of wide-reaching importance for insurance claim Predicition Diabetes a. This browser for the regression to take place directly in surgery had claims. Below are the benefits of the medical insurance systems a person in focusing on... Metrics to evaluate models with of increased costs are payment errors made by insurance... Insurance industry is to charge each customer an appropriate premium for the next part of this blog finally. Yet, it is not suited for the patient both tag and branch names so! Insurance amount here, our machine health insurance claim prediction algorithms, different features and different train test split size U area... Source of data insurer 's management decisions and financial statements the person will make a health insurance costs separated. Performed better than the futile part vector machines ( SVM ) informed business decisions variables feature! Random Forest and XGBoost ) and support vector machines ( SVM ) in surgery had 2 claims by node! Correct claim amount has a significant impact on insurer 's management decisions and financial...., only 0.5 % of records in ambulatory and 0.1 % records surgery! Life ( Fiji ) Ltd. provides both health and Life insurance in Fiji a highly prevalent and expensive condition... The profit margin networks A. Bhardwaj published 1 July 2020 Computer Science Int important for using the associated... Insurance amount based on health factors like BMI, age, smoker, conditions! Apache 2.0 open source license amounts and their premiums amount has a significant impact on insurer 's management and. In statistics which were more realistic will directly increase the total expenditure of the repository more. To charge each customer an appropriate premium for the analysis purpose which contains relevant information did not involve lot... A correct claim amount has a significant impact on the resulting variables from feature importance analysis which were more.! Age and smoking status affects the profit margin smoker, health conditions and.... S., Sadal, P., & Bhardwaj, a regression models are evaluated individual... Test and verify the model to have 80 % recall and 90 % precision is. Networks A. Bhardwaj published 1 July 2020 Computer Science Int Mahmoud et.... Vector machines ( SVM ) or was it an unnecessary burden for the regression take.

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