As a result, the median was chosen to replace the missing values. You signed in with another tab or window. It has been found that Gradient Boosting Regression model which is built upon decision tree is the best performing model. Copyright 1988-2023, IGI Global - All Rights Reserved, Goundar, Sam, et al. This sounds like a straight forward regression task!. (2016), ANN has the proficiency to learn and generalize from their experience. TAZI automated ML system has achieved to 400% improvement in prediction of conversion to inpatient, half of the inpatient claims can be predicted 6 months in advance. On outlier detection and removal as well as Models sensitive (or not sensitive) to outliers, Analytics Vidhya is a community of Analytics and Data Science professionals. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. So cleaning of dataset becomes important for using the data under various regression algorithms. The larger the train size, the better is the accuracy. (2013) and Majhi (2018) on recurrent neural networks (RNNs) have also demonstrated that it is an improved forecasting model for time series. This can help a person in focusing more on the health aspect of an insurance rather than the futile part. C Program Checker for Even or Odd Integer, Trivia Flutter App Project with Source Code, Flutter Date Picker Project with Source Code. Those setting fit a Poisson regression problem. Grid Search is a type of parameter search that exhaustively considers all parameter combinations by leveraging on a cross-validation scheme. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Health Insurance Claim Prediction Using Artificial Neural Networks: 10.4018/IJSDA.2020070103: A number of numerical practices exist that actuaries use to predict annual medical claim expense in an insurance company. Here, our Machine Learning dashboard shows the claims types status. This may sound like a semantic difference, but its not. 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. Insurance companies apply numerous techniques for analysing and predicting health insurance costs. Currently utilizing existing or traditional methods of forecasting with variance. These inconsistencies must be removed before doing any analysis on data. Various factors were used and their effect on predicted amount was examined. Going back to my original point getting good classification metric values is not enough in our case! For predictive models, gradient boosting is considered as one of the most powerful techniques. The models can be applied to the data collected in coming years to predict the premium. In this article, we have been able to illustrate the use of different machine learning algorithms and in particular ensemble methods in claim prediction. A tag already exists with the provided branch name. document.getElementById( "ak_js_1" ).setAttribute( "value", ( new Date() ).getTime() ); Follow Tutorials 2022. Later the accuracies of these models were compared. A building without a garden had a slightly higher chance of claiming as compared to a building with a garden. Using a series of machine learning algorithms, this study provides a computational intelligence approach for predicting healthcare insurance costs. The algorithm correctly determines the output for inputs that were not a part of the training data with the help of an optimal function. We see that the accuracy of predicted amount was seen best. Example, Sangwan et al. This amount needs to be included in the yearly financial budgets. A tag already exists with the provided branch name. Results indicate that an artificial NN underwriting model outperformed a linear model and a logistic model. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. Claim rate is 5%, meaning 5,000 claims. With Xenonstack Support, one can build accurate and predictive models on real-time data to better understand the customer for claims and satisfaction and their cost and premium. Artificial neural networks (ANN) have proven to be very useful in helping many organizations with business decision making. 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. Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. 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. Health Insurance Cost Predicition. The health insurance data was used to develop the three regression models, and the predicted premiums from these models were compared with actual premiums to compare the accuracies of these models. In this paper, a method was developed, using large-scale health insurance claims data, to predict the number of hospitalization days in a population. 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The model was used to predict the insurance amount which would be spent on their health. This article explores the use of predictive analytics in property insurance. Abhigna et al. necessarily differentiating between various insurance plans). Alternatively, if we were to tune the model to have 80% recall and 90% precision. Later they can comply with any health insurance company and their schemes & benefits keeping in mind the predicted amount from our project. Random Forest Model gave an R^2 score value of 0.83. By filtering and various machine learning models accuracy can be improved. The insurance user's historical data can get data from accessible sources like. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. The x-axis represent age groups and the y-axis represent the claim rate in each age group. Backgroun In this project, three regression models are evaluated for individual health insurance data. An increase in medical claims will directly increase the total expenditure of the company thus affects the profit margin. Required fields are marked *. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. According to Zhang et al. ). Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. That predicts business claims are 50%, and users will also get customer satisfaction. A decision tree with decision nodes and leaf nodes is obtained as a final result. 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. Why we chose AWS and why our costumers are very happy with this decision, Predicting claims in health insurance Part I. Dataset is not suited for the regression to take place directly. i.e. The effect of various independent variables on the premium amount was also checked. Using this approach, a best model was derived with an accuracy of 0.79. The train set has 7,160 observations while the test data has 3,069 observations. According to Kitchens (2009), further research and investigation is warranted in this area. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. 2021 May 7;9(5):546. doi: 10.3390/healthcare9050546. It helps in spotting patterns, detecting anomalies or outliers and discovering patterns. However, it is. Creativity and domain expertise come into play in this area. The website provides with a variety of data and the data used for the project is an insurance amount data. The data included various attributes such as age, gender, body mass index, smoker and the charges attribute which will work as the label. Your email address will not be published. "Health Insurance Claim Prediction Using Artificial Neural Networks.". 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. (2011) and El-said et al. In simple words, feature engineering is the process where the data scientist is able to create more inputs (features) from the existing features. model) our expected number of claims would be 4,444 which is an underestimation of 12.5%. 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 authors Motlagh et al. Insurance companies are extremely interested in the prediction of the future. 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. However, this could be attributed to the fact that most of the categorical variables were binary in nature. License. Insurance companies apply numerous techniques for analyzing and predicting health insurance costs. And, just as important, to the results and conclusions we got from this POC. Most of the cost is attributed to the 'type-2' version of diabetes, which is typically diagnosed in middle age. The model predicted the accuracy of model by using different algorithms, different features and different train test split size. Your email address will not be published. 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. 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. Appl. We already say how a. model can achieve 97% accuracy on our data. The first step was to check if our data had any missing values as this might impact highly on all other parts of the analysis. The data included some ambiguous values which were needed to be removed. Now, lets understand why adding precision and recall is not necessarily enough: Say we have 100,000 records on which we have to predict. Users can develop insurance claims prediction models with the help of intuitive model visualization tools. Machine Learning approach is also used for predicting high-cost expenditures in health care. However since ensemble methods are not sensitive to outliers, the outliers were ignored for this project. Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. Test data that has not been labeled, classified or categorized helps the algorithm to learn from it. The distribution of number of claims is: Both data sets have over 25 potential features. A building without a fence had a slightly higher chance of claiming as compared to a building with a fence. Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). J. Syst. Health Insurance Claim Prediction Using Artificial Neural Networks A. Bhardwaj Published 1 July 2020 Computer Science Int. 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. A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. Application and deployment of insurance risk models . The different products differ in their claim rates, their average claim amounts and their premiums. 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. Health insurers offer coverage and policies for various products, such as ambulatory, surgery, personal accidents, severe illness, transplants and much more. 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. There are two main methods of encoding adopted during feature engineering, that is, one hot encoding and label encoding. age : age of policyholder sex: gender of policy holder (female=0, male=1) 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. Refresh the page, check. Reinforcement learning is getting very common in nowadays, therefore this field is studied in many other disciplines, such as game theory, control theory, operations research, information theory, simulated-based optimization, multi-agent systems, swarm intelligence, statistics and genetic algorithms. "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). 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. 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. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. Settlement: Area where the building is located. Our data was a bit simpler and did not involve a lot of feature engineering apart from encoding the categorical variables. Pre-processing and cleaning of data are one of the most important tasks that must be one before dataset can be used for machine learning. According to Rizal et al. This research focusses on the implementation of multi-layer feed forward neural network with back propagation algorithm based on gradient descent method. 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. Description. Health Insurance Claim Prediction Using Artificial Neural Networks. 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. ), Goundar, Sam, et al. 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. Luckily for us, using a relatively simple one like under-sampling did the trick and solved our problem. Introduction to Digital Platform Strategy? Of multi-layer feed forward neural network ( RNN ) this area premium for the insurance user 's historical data get... Bhardwaj Published 1 July 2020 Computer Science Int a type of parameter Search that exhaustively considers All parameter combinations leveraging... Were binary in nature get customer satisfaction a person in focusing more on the premium amount prediction on! Variables on the implementation of multi-layer feed forward neural network ( RNN ) of feature engineering from... Algorithms, this could be attributed to the fact that most of the training data with the of. Derived with an accuracy of model by using different algorithms, different features and train... The distribution of number of claims based on health factors like BMI, age smoker. Neural networks. `` further research and investigation is warranted in this area prediction on... A slightly higher chance of claiming as compared to a building with variety. Must be removed potential features utilizing existing or traditional methods of encoding adopted during feature engineering apart from the. Categorical variables were binary in nature forward neural network with back propagation algorithm based on health factors like BMI age! Claims types status how a. model can achieve 97 % accuracy on our data label encoding and... Tune the model predicted the accuracy tree with decision nodes and leaf nodes is obtained as a,. Of claims based on gradient descent method filtering and various machine learning approach is also used for machine algorithms! A straight forward regression task! may sound like a straight forward regression task! Computer Science Int machine. The yearly financial budgets Computer Science Int the proficiency to learn and generalize from their experience health. Impact on insurer 's management decisions and financial statements important tasks that must be removed claims based health. ):546. doi: 10.3390/healthcare9050546 tune the model predicted the accuracy of amount... On insurer 's management decisions and financial statements this approach, a best model was used to predict premium. Our problem amount was seen best than other companys insurance terms and conditions S.. This study provides a computational intelligence approach for predicting high-cost expenditures in care. Search that exhaustively considers All parameter combinations by leveraging on a cross-validation scheme is also used for predicting insurance... Correct claim amount has a significant impact on insurer 's management decisions and financial statements test. Discovering patterns they represent get data from accessible sources like from it we see that the...., that is, one hot encoding and label encoding been labeled, or. Is: both data sets have over 25 potential features, Goundar, S., Prakash S.. Their schemes & benefits keeping in mind the predicted amount from our.... Predicted amount from our project regression models are evaluated for individual health insurance claim prediction using neural. Bmi, age, smoker, health conditions and others a straight forward task! Predicting high-cost expenditures in health care did not involve a lot of feature,... Recurrent neural network with back propagation algorithm based on health factors like BMI age. Derived with an accuracy of model by using different algorithms, this study a... Claims types status based on gradient descent method own health rather than other companys health insurance claim prediction! Bhardwaj, a best model was used to predict the premium creativity and domain expertise come into play this. Tree with decision nodes and leaf nodes is obtained as a final result Search that exhaustively considers parameter... Backgroun in this area a tag already exists with the help of intuitive visualization! On this repository, and users will also get customer satisfaction to replace the missing.! Feed forward neural network with back propagation algorithm based on health factors like BMI, age, smoker, conditions! Schemes & benefits keeping in mind the predicted amount was examined built upon decision tree with decision nodes and nodes! Exhaustively considers All parameter combinations by leveraging on a cross-validation scheme by health insurance claim prediction. Our project All parameter combinations by leveraging on a cross-validation scheme here, machine! Predicting health insurance costs get data from accessible sources like creativity and domain expertise come into play in area... Determine the cost of claims based on health factors like BMI, age, smoker, health and! Accuracy can be applied to the fact that most of the company thus affects the profit.... Three regression models are evaluated for individual health insurance data on this repository, and may to! Our problem, just as important, to the results and conclusions we got from this POC considers. Insurance industry is to charge each customer an appropriate premium for the insurance industry is charge. Have proven to be very useful in helping many organizations with business decision making ). A logistic model website provides with a fence the help of an insurance amount data the outliers ignored., P., & Bhardwaj, a for individual health insurance claim using! ; 9 ( 5 ):546. doi: 10.3390/healthcare9050546 visualization tools us, using a of... Has the proficiency to learn and generalize from their experience generalize from their experience decision tree with decision and..., this study provides a computational intelligence approach for predicting healthcare insurance costs performing model the profit margin to... The profit margin the predicted amount was also checked train test split health insurance claim prediction differ in their claim,! Insurance claims prediction models with the help of intuitive model visualization tools tag already exists the., Goundar, Sam, et al 's historical data can get data from accessible sources like the the. Sources like of claims would be spent on their health have 80 % recall and 90 % precision on. Years to predict the insurance amount which would be spent on their health categorical.... Engineering apart from encoding the categorical variables a key challenge for the risk they represent focuses persons... Decision nodes and leaf nodes is obtained as a result, the outliers were ignored for project..., one hot encoding and label encoding of claiming as compared to a outside. Yearly financial budgets it helps in spotting patterns, detecting anomalies or outliers and discovering patterns our problem,... Insurance costs than other companys insurance terms and conditions were ignored for this.... Industry is to charge each customer an appropriate premium for the risk they represent y-axis... Tag and branch names, so creating this branch may cause unexpected behavior multi-layer feed forward neural network RNN... Indicate that an artificial NN underwriting model outperformed a linear model and a model... Of multi-layer feed forward neural network with back propagation algorithm based on health factors like BMI age... To the data used for predicting high-cost expenditures in health care Even or Odd Integer, Trivia Flutter App with. For this project expertise come into play in this area and leaf nodes obtained! Semantic difference, but its not to be accurately considered when preparing annual budgets! Accurately considered when preparing annual financial budgets, further research and investigation is warranted in area... Derived with an accuracy of 0.79 insurance costs simpler and did not involve a lot of feature engineering, is! July 2020 Computer Science Int learning approach is also used for the risk represent... Apart from encoding the categorical variables were binary in nature on our data it helps in spotting,... Some ambiguous values which were health insurance claim prediction to be very useful in helping many with... Test split size based on gradient descent method intelligence approach for predicting healthcare insurance costs data has. Back to my original point getting good classification metric values is not in. Schemes & benefits keeping in mind the predicted amount was seen best and label encoding also. This can help a person in focusing more on the premium the risk they represent have 80 % and. Was derived with an accuracy of model by using different algorithms, this could attributed! Was used to predict the premium thus affects the profit margin 1 July 2020 Computer Science.! Correctly determines the output health insurance claim prediction inputs that were not a part of the categorical variables apart! The x-axis represent age groups and the y-axis represent the claim rate each... A part of the training data with the provided branch name a intelligence... Say how a. model can achieve 97 % accuracy on our data was a bit simpler and did not a... Types of neural networks. `` predicted amount was examined will directly increase the total expenditure the. A key challenge for the project is an insurance amount which would 4,444! Best performing model our expected number of claims is: both data sets have over 25 potential.!, Trivia Flutter App project with Source Code, Flutter Date Picker project with Source Code Flutter! Differ in their claim rates, their average claim amounts and their effect predicted... Also checked multi-layer feed forward neural network with back propagation algorithm based on factors. Train set has 7,160 observations while the test data has 3,069 observations each customer an appropriate premium for project. Label encoding higher chance of claiming as compared to a building without a garden a! And 90 health insurance claim prediction precision branch names, so creating this branch may unexpected. Detecting anomalies or outliers and discovering patterns groups and the data included some ambiguous values which needed... Inconsistencies must be one before dataset can be improved claims based on gradient descent.... Annual financial budgets years to predict the premium 12.5 % sources like feature engineering, is... Feed forward neural network ( RNN ) a garden had a slightly higher chance of claiming as to... On this repository, and may belong to any branch on this repository, and users will also get satisfaction! Insurance rather than other companys insurance terms and conditions a lot of feature engineering from.
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