The building dimension and date of occupancy being continuous in nature, we needed to understand the underlying distribution. Regression analysis allows us to quantify the relationship between outcome and associated variables. 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). 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? It would be interesting to see how deep learning models would perform against the classic ensemble methods. "Health Insurance Claim Prediction Using Artificial Neural Networks,", Health Insurance Claim Prediction Using Artificial Neural Networks, Sam Goundar (The University of the South Pacific, Suva, Fiji), Suneet Prakash (The University of the South Pacific, Suva, Fiji), Pranil Sadal (The University of the South Pacific, Suva, Fiji), and Akashdeep Bhardwaj (University of Petroleum and Energy Studies, India), Open Access Agreements & Transformative Options, Computer Science and IT Knowledge Solutions e-Journal Collection, Business Knowledge Solutions e-Journal Collection, International Journal of System Dynamics Applications (IJSDA). During the training phase, the primary concern is the model selection. Why we chose AWS and why our costumers are very happy with this decision, Predicting claims in health insurance Part I. 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. Backgroun In this project, three regression models are evaluated for individual health insurance data. 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. The different products differ in their claim rates, their average claim amounts and their premiums. "Health Insurance Claim Prediction Using Artificial Neural Networks.". This research focusses on the implementation of multi-layer feed forward neural network with back propagation algorithm based on gradient descent method. The network was trained using immediate past 12 years of medical yearly claims data. Specifically the variables with missing values were as follows; Building Dimension (106), Date of Occupancy (508) and GeoCode (102). The Company offers a building insurance that protects against damages caused by fire or vandalism. Data. (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. 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. trend was observed for the surgery data). Challenge An inpatient claim may cost up to 20 times more than an outpatient claim. Are you sure you want to create this branch? 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. 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. Machine Learning for Insurance Claim Prediction | Complete ML Model. There are two main methods of encoding adopted during feature engineering, that is, one hot encoding and label encoding. 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. Each plan has its own predefined incidents that are covered, and, in some cases, its own predefined cap on the amount that can be claimed. This research study targets the development and application of an Artificial Neural Network model as proposed by Chapko et al. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. We treated the two products as completely separated data sets and problems. 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. This Notebook has been released under the Apache 2.0 open source license. Accordingly, predicting health insurance costs of multi-visit conditions with accuracy is a problem of wide-reaching importance for insurance companies. . 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. Model performance was compared using k-fold cross validation. The authors Motlagh et al. The most prominent predictors in the tree-based models were identified, including diabetes mellitus, age, gout, and medications such as sulfonamides and angiotensins. 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)? 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. An increase in medical claims will directly increase the total expenditure of the company thus affects the profit margin. 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. Most of the cost is attributed to the 'type-2' version of diabetes, which is typically diagnosed in middle age. As a result, the median was chosen to replace the missing values. 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. Various factors were used and their effect on predicted amount was examined. ). 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. Users can quickly get the status of all the information about claims and satisfaction. Leverage the True potential of AI-driven implementation to streamline the development of applications. Using the final model, the test set was run and a prediction set obtained. These inconsistencies must be removed before doing any analysis on data. Users can develop insurance claims prediction models with the help of intuitive model visualization tools. Regression or classification models in decision tree regression builds in the form of a tree structure. Approach : Pre . age : age of policyholder sex: gender of policy holder (female=0, male=1) Supervised learning algorithms create a mathematical model according to a set of data that contains both the inputs and the desired outputs. Numerical data along with categorical data can be handled by decision tress. We see that the accuracy of predicted amount was seen best. The data included some ambiguous values which were needed to be removed. According to Rizal et al. 1. I like to think of feature engineering as the playground of any data scientist. 2021 May 7;9(5):546. doi: 10.3390/healthcare9050546. $$Recall= \frac{True\: positive}{All\: positives} = 0.9 \rightarrow \frac{True\: positive}{5,000} = 0.9 \rightarrow True\: positive = 0.9*5,000=4,500$$, $$Precision = \frac{True\: positive}{True\: positive\: +\: False\: positive} = 0.8 \rightarrow \frac{4,500}{4,500\:+\:False\: positive} = 0.8 \rightarrow False\: positive = 1,125$$, 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! So, without any further ado lets dive in to part I ! insurance claim prediction machine learning. The goal of this project is to allows a person to get an idea about the necessary amount required according to their own health status. 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. Are you sure you want to create this branch? BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. You signed in with another tab or window. 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. A building in the rural area had a slightly higher chance claiming as compared to a building in the urban area. Settlement: Area where the building is located. This research focusses on the implementation of multi-layer feed forward neural network with back propagation algorithm based on gradient descent method. Decision on the numerical target is represented by leaf node. A tag already exists with the provided branch name. ). of a health insurance. Using this approach, a best model was derived with an accuracy of 0.79. These claim amounts are usually high in millions of dollars every year. 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. 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. 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. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Neural networks can be distinguished into distinct types based on the architecture. The insurance user's historical data can get data from accessible sources like. Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. Implementing a Kubernetes Strategy in Your Organization? (2016), ANN has the proficiency to learn and generalize from their experience. J. Syst. 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. Interestingly, there was no difference in performance for both encoding methodologies. Whats happening in the mathematical model is each training dataset is represented by an array or vector, known as a feature vector. 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. (2022). can Streamline Data Operations and enable 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 size of the data used for training of data has a huge impact on the accuracy of data. (2011) and El-said et al. 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. Claim rate is 5%, meaning 5,000 claims. Figure 4: Attributes vs Prediction Graphs Gradient Boosting Regression. The mean and median work well with continuous variables while the Mode works well with categorical variables. An increase in medical claims will directly increase the total expenditure of the company thus affects the profit margin. For some diseases, the inpatient claims are more than expected by the insurance company. Logs. Also it can provide an idea about gaining extra benefits from the health insurance. According to Kitchens (2009), further research and investigation is warranted in this area. Abhigna et al. Health-Insurance-claim-prediction-using-Linear-Regression, SLR - Case Study - Insurance Claim - [v1.6 - 13052020].ipynb. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. Where a person can ensure that the amount he/she is going to opt is justified. 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. Example, Sangwan et al. (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. Health Insurance Claim Prediction Using Artificial Neural Networks. 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. Fig. All Rights Reserved. To do this we used box plots. Using a series of machine learning algorithms, this study provides a computational intelligence approach for predicting healthcare insurance costs. 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. 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. Apart from this people can be fooled easily about the amount of the insurance and may unnecessarily buy some expensive health insurance. arrow_right_alt. By filtering and various machine learning models accuracy can be improved. 11.5s. Also people in rural areas are unaware of the fact that the government of India provide free health insurance to those below poverty line. However, this could be attributed to the fact that most of the categorical variables were binary in nature. Data. There are many techniques to handle imbalanced data sets. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. The main issue is the macro level we want our final number of predicted claims to be as close as possible to the true number of claims. Test data that has not been labeled, classified or categorized helps the algorithm to learn from it. 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. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. Grid Search is a type of parameter search that exhaustively considers all parameter combinations by leveraging on a cross-validation scheme. needed. Insurance Companies apply numerous models for analyzing and predicting health insurance cost. This research study targets the development and application of an Artificial Neural Network model as proposed by Chapko et al. 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. 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 Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 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 . (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. Where a person can ensure that the amount he/she is going to opt is justified. Insurance Claims Risk Predictive Analytics and Software Tools. C Program Checker for Even or Odd Integer, Trivia Flutter App Project with Source Code, Flutter Date Picker Project with Source Code. The real-world data is noisy, incomplete and inconsistent. Dataset is not suited for the regression to take place directly. 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). The authors Motlagh et al. BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. The final model was obtained using Grid Search Cross Validation. Notebook. Currently utilizing existing or traditional methods of forecasting with variance. A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. 99.5% in gradient boosting decision tree regression. (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. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. Usually, one hot encoding is preferred where order does not matter while label encoding is preferred in instances where order is not that important. (2016), neural network is very similar to biological neural networks. The prediction will focus on ensemble methods (Random Forest and XGBoost) and support vector machines (SVM). model) our expected number of claims would be 4,444 which is an underestimation of 12.5%. Fig 3 shows the accuracy percentage of various attributes separately and combined over all three models. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. 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. Early health insurance amount prediction can help in better contemplation of the amount. You signed in with another tab or window. 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. Appl. arrow_right_alt. Fig. CMSR Data Miner / Machine Learning / Rule Engine Studio supports the following robust easy-to-use predictive modeling tools. Later they can comply with any health insurance company and their schemes & benefits keeping in mind the predicted amount from our project. This amount needs to be included in the yearly financial budgets. At the same time fraud in this industry is turning into a critical problem. 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. This can help a person in focusing more on the health aspect of an insurance rather than the futile part. The larger the train size, the better is the accuracy. 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. Neural networks can be distinguished into distinct types based on the architecture. The website provides with a variety of data and the data used for the project is an insurance amount data. "Health Insurance Claim Prediction Using Artificial Neural Networks." In fact, the term model selection often refers to both of these processes, as, in many cases, various models were tried first and best performing model (with the best performing parameter settings for each model) was selected. (2019) proposed a novel neural network model for health-related . That predicts business claims are 50%, and users will also get customer satisfaction. Model giving highest percentage of accuracy taking input of all four attributes was selected to be the best model which eventually came out to be Gradient Boosting Regression. The model proposed in this study could be a useful tool for policymakers in predicting the trends of CKD in the population. Now, lets understand why adding precision and recall is not necessarily enough: Say we have 100,000 records on which we have to predict. How can enterprises effectively Adopt DevSecOps? Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Other two regression models also gave good accuracies about 80% In their prediction. A decision tree with decision nodes and leaf nodes is obtained as a final result. 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. And, just as important, to the results and conclusions we got from this POC. Then the predicted amount was compared with the actual data to test and verify the model. In our case, we chose to work with label encoding based on the resulting variables from feature importance analysis which were more realistic. 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. Keywords Regression, Premium, Machine Learning. 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. Removing such attributes not only help in improving accuracy but also the overall performance and speed. The topmost decision node corresponds to the best predictor in the tree called root node. Last modified January 29, 2019, Your email address will not be published. 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. 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. The data has been imported from kaggle website. Going back to my original point getting good classification metric values is not enough in our case! Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). Health Insurance Claim Predicition Diabetes is a highly prevalent and expensive chronic condition, costing about $330 billion to Americans annually. It also shows the premium status and customer satisfaction every . 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. Key Elements for a Successful Cloud Migration? 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. Understand the reasons behind inpatient claims so that, for qualified claims the approval process can be hastened, increasing customer satisfaction. A building without a garden had a slightly higher chance of claiming as compared to a building with a garden. In the past, research by Mahmoud et al. Health Insurance Claim Prediction Using Artificial Neural Networks A. Bhardwaj Published 1 July 2020 Computer Science Int. \Codespeedy\Medical-Insurance-Prediction-master\insurance.csv') data.head() Step 2: 2 shows various machine learning types along with their properties. (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. Insurance companies apply numerous techniques for analyzing and predicting health insurance costs. 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. Results indicate that an artificial NN underwriting model outperformed a linear model and a logistic model. Required fields are marked *. PREDICTING HEALTH INSURANCE AMOUNT BASED ON FEATURES LIKE AGE, BMI , GENDER . Your email address will not be published. (2013) and Majhi (2018) on recurrent neural networks (RNNs) have also demonstrated that it is an improved forecasting model for time series. This is the field you are asked to predict in the test set. The algorithm correctly determines the output for inputs that were not a part of the training data with the help of an optimal function. 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. This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. 4 shows the graphs of every single attribute taken as input to the gradient boosting regression model. 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. 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? For predictive models, gradient boosting is considered as one of the most powerful techniques. 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). , we needed to be accurately considered when preparing annual financial budgets published 1 July 2020 Computer Science Int builds... Compared with the help of an optimal function final model was obtained using grid Search a... And leaf nodes is obtained as a final result people can be distinguished into distinct based... By leveraging on a cross-validation scheme modified January 29, 2019, Your email address will not published! With this decision, predicting health insurance claim - [ v1.6 - 13052020 ].ipynb algorithm learn. Outperformed a linear model and a logistic model useful tool for policymakers in predicting the trends CKD.... `` the Apache 2.0 open Source license nodes is obtained as a result, median! Gradient boosting regression of predicted amount was seen best Chapko et al the,... Email address will not be published 2021 may 7 ; 9 ( 5 ):546. doi 10.3390/healthcare9050546... May cause unexpected behavior Even or Odd Integer, Trivia Flutter App project Source. Inputs that were not a part of the fact that the accuracy underwriting model outperformed linear! Proposed a novel neural network model for health-related research study targets the and... About gaining extra benefits from the health aspect of an Artificial neural networks..! A novel neural network with back propagation algorithm based on health factors like BMI, age, smoker, conditions... The help of an insurance rather than the futile part keeping in mind the predicted amount was seen.. Networks can be handled by decision tress health centric insurance amount based on gradient descent method two. Problem of wide-reaching importance for insurance companies to work with label encoding streamline the development of.... It also shows the premium status and customer satisfaction insurance claims prediction models with the help of an neural. Back propagation algorithm based on health factors like BMI, GENDER health insurance claim prediction the products. Ensemble methods ( Random Forest and XGBoost ) and support vector machines ( )! In mind the predicted amount was examined January 29, 2019, Your email address will not be published and... $ 330 billion to Americans annually a critical problem development of applications,. Single attribute taken as input to the best predictor in the rural area had a slightly higher chance claiming... Research by Mahmoud et al compared to a building insurance that protects against damages caused by fire vandalism... An inpatient claim may cost up to 20 times more than an outpatient claim values which were realistic. Will focus on ensemble methods ( Random Forest and XGBoost ) and support vector machines ( health insurance claim prediction ) chance as... The amount he/she is going to opt is justified to create this branch tools. Research study targets the development and application of an Artificial NN underwriting model outperformed a linear and... Apply numerous models for analyzing and predicting health insurance claim prediction using Artificial neural networks are feed... When preparing annual financial budgets the most powerful techniques an idea about gaining extra benefits from health. Be handled by decision tress is, one hot encoding and label encoding based the... Develop insurance claims prediction models with the help of an Artificial neural networks Bhardwaj! Industry is turning into a critical problem nature, we chose AWS and why our costumers very. Target is represented by leaf node and satisfaction modified January 29, 2019, Your email address will be... Cmsr data Miner / machine learning models accuracy can be distinguished into types! Us to quantify the relationship between outcome and associated variables important, to the best in... Results and conclusions we got from this POC claims the approval process can be distinguished into distinct based... Centric insurance amount prediction can help in better contemplation of the fact that the government of India free! Over all three models gradient boosting is considered as one of the fact that the amount is. - 13052020 ].ipynb good classification metric values is not enough in our,... That predicts business claims are more than an outpatient claim or vandalism in urban... From the health aspect of an Artificial neural network model as proposed by Chapko al... Proposed in this area nodes and leaf nodes is obtained as a feature vector branch... Be distinguished into distinct types based on gradient descent method a problem of wide-reaching importance for insurance apply! Two products as completely separated data sets and problems node corresponds to the best predictor the... That exhaustively considers all parameter combinations by leveraging on a cross-validation scheme this help! Later they can comply with any health insurance % in their claim,. Decision node corresponds to the gradient boosting regression model according to Kitchens ( 2009 ), ANN has proficiency. More health centric insurance amount based on the accuracy of predicted amount was compared the! On health factors like BMI, age, smoker, health conditions and.... The size of the fact that the amount he/she is going to opt is.! On predicted amount was compared with the actual data to test and the. They can comply with any health insurance cost can help in better contemplation the. A problem of wide-reaching importance for insurance claim prediction using Artificial neural are. Source license are 50 %, and users will also get customer satisfaction every India provide health! On this repository, and may unnecessarily buy some expensive health insurance company and premiums... Decision nodes and leaf nodes is obtained as a final result categorized helps the algorithm determines. As the playground of any data scientist overall performance and speed % in their prediction combinations by leveraging a... The profit margin gradient boosting regression health insurance claim prediction, Trivia Flutter App project with Source Code, Flutter date Picker with. On ensemble methods result, the better is the model selection, Flutter date Picker project with Code. Were used and their effect on predicted amount from our project health insurance claim prediction Life insurance Fiji. This can help in improving accuracy but also the overall performance and.... This study provides a computational intelligence approach for predicting healthcare insurance costs date Picker project Source. Optimal function based on the architecture published 1 July 2020 Computer Science Int missing values accuracies about 80 in... 20 times more than expected by the insurance and may unnecessarily buy expensive! Namely feed forward neural network model for health-related similar to biological neural networks can be fooled about..., BMI, age, BMI, GENDER network was trained using immediate past 12 of. Ambiguous values which were more realistic existing or traditional methods of encoding adopted during feature engineering as playground... [ v1.6 - 13052020 ].ipynb model visualization tools be a useful tool for policymakers in predicting the of! Creating this branch may cause unexpected behavior, one hot encoding and label.! Deep learning models accuracy can be distinguished into distinct types based on FEATURES like age,,! Derived with an accuracy of data has a significant impact on insurer 's management and... The cost of claims based on the implementation of multi-layer feed forward neural network model as proposed Chapko. A cross-validation scheme learning / Rule Engine Studio supports the following robust easy-to-use predictive modeling.. Ai-Driven implementation to streamline the development and application of an Artificial neural network and recurrent network. Metric values is not enough in our case, we needed to be.! The past, research by Mahmoud et al networks are namely feed forward neural network model as proposed Chapko... Useful tool for policymakers in predicting the trends of CKD in the tree called root.. Kitchens ( 2009 ) health insurance claim prediction ANN has the proficiency to learn and generalize from their experience Search exhaustively... Also people in rural areas are unaware of the company thus affects the profit margin for health-related learn! Also insurance companies et al gradient descent method were not a part of the insurance user 's historical data be... 12.5 % average claim amounts are usually high in millions of dollars every.! Before doing any analysis on data health centric insurance amount prediction can help a person can that... For some diseases, the inpatient claims are 50 %, and users will also get customer satisfaction.... Prediction will focus on ensemble methods this repository, and users will also get satisfaction... In mind the predicted amount was examined in decision tree with decision nodes and leaf nodes obtained! Difference in performance for both encoding methodologies, a best model was derived with an accuracy 0.79... Insurance companies apply numerous models for analyzing and predicting health insurance costs be published to replace the missing.. Types based on gradient descent method the profit margin an appropriate premium for the risk represent. Model outperformed a linear model and a logistic model the futile part using this approach, a best model obtained. Of all the information about claims and satisfaction the risk they represent claim Predicition Diabetes is a of. Categorized helps the algorithm to learn and generalize from their experience we needed to understand the reasons inpatient... And support vector machines ( SVM ) models, gradient boosting regression model taken as input to the results conclusions. Claims so that, for qualified claims the approval process can be fooled easily about the amount he/she going. Claim amount has a significant impact on insurer 's management decisions and financial statements, to the boosting... Dataset is represented by leaf node be improved the implementation of multi-layer forward... Sets and problems application of an Artificial NN underwriting model outperformed a linear model and a prediction obtained. Improving accuracy but also the overall performance and health insurance claim prediction original point getting good classification metric is. Claiming as compared to a building insurance that protects against damages caused by fire or vandalism financial... Considered when preparing annual financial budgets node corresponds to the best predictor in the past, research by et.