health insurance claim prediction
Required fields are marked *. Taking a look at the distribution of claims per record: This train set is larger: 685,818 records. An inpatient claim may cost up to 20 times more than an outpatient claim. Implementing a Kubernetes Strategy in Your Organization? 1 input and 0 output. Currently utilizing existing or traditional methods of forecasting with variance. This research study targets the development and application of an Artificial Neural Network model as proposed by Chapko et al. 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. Whats happening in the mathematical model is each training dataset is represented by an array or vector, known as a feature vector. Neural networks can be distinguished into distinct types based on the architecture. Accuracy defines the degree of correctness of the predicted value of the insurance amount. 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. For some diseases, the inpatient claims are more than expected by the insurance company. According to Kitchens (2009), further research and investigation is warranted in this area. The diagnosis set is going to be expanded to include more diseases. Comments (7) Run. Machine Learning for Insurance Claim Prediction | Complete ML Model. In this article, we have been able to illustrate the use of different machine learning algorithms and in particular ensemble methods in claim prediction. Each plan has its own predefined . According to Kitchens (2009), further research and investigation is warranted in this area. Claim rate is 5%, meaning 5,000 claims. 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. Open access articles are freely available for download, Volume 12: 1 Issue (2023): Forthcoming, Available for Pre-Order, Volume 11: 5 Issues (2022): Forthcoming, Available for Pre-Order, Volume 10: 4 Issues (2021): Forthcoming, Available for Pre-Order, Volume 9: 4 Issues (2020): Forthcoming, Available for Pre-Order, Volume 8: 4 Issues (2019): Forthcoming, Available for Pre-Order, Volume 7: 4 Issues (2018): Forthcoming, Available for Pre-Order, Volume 6: 4 Issues (2017): Forthcoming, Available for Pre-Order, Volume 5: 4 Issues (2016): Forthcoming, Available for Pre-Order, Volume 4: 4 Issues (2015): Forthcoming, Available for Pre-Order, Volume 3: 4 Issues (2014): Forthcoming, Available for Pre-Order, Volume 2: 4 Issues (2013): Forthcoming, Available for Pre-Order, Volume 1: 4 Issues (2012): Forthcoming, Available for Pre-Order, Copyright 1988-2023, IGI Global - All Rights Reserved, Goundar, Sam, et al. "Health Insurance Claim Prediction Using Artificial Neural Networks.". A tag already exists with the provided branch name. Insurance companies apply numerous techniques for analyzing and predicting health insurance costs. 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. necessarily differentiating between various insurance plans). In the next part of this blog well finally get to the modeling process! Most of the cost is attributed to the 'type-2' version of diabetes, which is typically diagnosed in middle age. Now, lets also say that weve built a mode, and its relatively good: it has 80% precision and 90% recall. ), Goundar, Sam, et al. Achieve Unified Customer Experience with efficient and intelligent insight-driven solutions. The website provides with a variety of data and the data used for the project is an insurance amount data. Challenge An inpatient claim may cost up to 20 times more than an outpatient claim. The model predicts the premium amount using multiple algorithms and shows the effect of each attribute on the predicted value. Then the predicted amount was compared with the actual data to test and verify the model. 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. 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. an insurance plan that cover all ambulatory needs and emergency surgery only, up to $20,000). 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. arrow_right_alt. needed. It is based on a knowledge based challenge posted on the Zindi platform based on the Olusola Insurance Company. This research focusses on the implementation of multi-layer feed forward neural network with back propagation algorithm based on gradient descent method. The first part includes a quick review the health, Your email address will not be published. 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. Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. It would be interesting to see how deep learning models would perform against the classic ensemble methods. (2017) state that artificial neural network (ANN) has been constructed on the human brain structure with very useful and effective pattern classification capabilities. Where a person can ensure that the amount he/she is going to opt is justified. Take for example the, feature. Description. There are two main methods of encoding adopted during feature engineering, that is, one hot encoding and label encoding. Example, Sangwan et al. The dataset is comprised of 1338 records with 6 attributes. Now, lets understand why adding precision and recall is not necessarily enough: Say we have 100,000 records on which we have to predict. Goundar, Sam, et al. These decision nodes have two or more branches, each representing values for the attribute tested. Insurance Claim Prediction Problem Statement A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. So, without any further ado lets dive in to part I ! 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 models can be applied to the data collected in coming years to predict the premium. Fig. A building without a fence had a slightly higher chance of claiming as compared to a building with a fence. 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. As a result, the median was chosen to replace the missing values. 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. A building without a garden had a slightly higher chance of claiming as compared to a building with a garden. The data included various attributes such as age, gender, body mass index, smoker and the charges attribute which will work as the label. Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. Artificial neural networks (ANN) have proven to be very useful in helping many organizations with business decision making. It is very complex method and some rural people either buy some private health insurance or do not invest money in health insurance at all. Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. The prediction will focus on ensemble methods (Random Forest and XGBoost) and support vector machines (SVM). To do this we used box plots. According to Zhang et al. 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. Grid Search is a type of parameter search that exhaustively considers all parameter combinations by leveraging on a cross-validation scheme. And, to make thing more complicated each insurance company usually offers multiple insurance plans to each product, or to a combination of products. Early health insurance amount prediction can help in better contemplation of the amount needed. 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. 2021 May 7;9(5):546. doi: 10.3390/healthcare9050546. This sounds like a straight forward regression task!. This involves choosing the best modelling approach for the task, or the best parameter settings for a given model. 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. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 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)? The size of the data used for training of data has a huge impact on the accuracy of data. arrow_right_alt. Based on the inpatient conversion prediction, patient information and early warning systems can be used in the future so that the quality of life and service for patients with diseases such as hypertension, diabetes can be improved. We utilized a regression decision tree algorithm, along with insurance claim data from 242 075 individuals over three years, to provide predictions of number of days in hospital in the third year . Adapt to new evolving tech stack solutions to ensure informed business decisions. A tag already exists with the provided branch name. The network was trained using immediate past 12 years of medical yearly claims data. Test data that has not been labeled, classified or categorized helps the algorithm to learn from it. The final model was obtained using Grid Search Cross Validation. 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 case, we used several visualization methods to better understand our data set. insurance claim prediction machine learning. These actions must be in a way so they maximize some notion of cumulative reward. 99.5% in gradient boosting decision tree regression. 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. We found out that while they do have many differences and should not be modeled together they also have enough similarities such that the best methodology for the Surgery analysis was also the best for the Ambulatory insurance. 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. Various factors were used and their effect on predicted amount was examined. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. And its also not even the main issue. "Health Insurance Claim Prediction Using Artificial Neural Networks.". This amount needs to be included in DATASET USED The primary source of data for this project was . Figure 4: Attributes vs Prediction Graphs Gradient Boosting Regression. 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. How can enterprises effectively Adopt DevSecOps? BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. Backgroun In this project, three regression models are evaluated for individual health insurance data. 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. Decision on the numerical target is represented by leaf node. (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. The real-world data is noisy, incomplete and inconsistent. The main application of unsupervised learning is density estimation in statistics. 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. Dyn. 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. 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 . ). (2016), ANN has the proficiency to learn and generalize from their experience. This amount needs to be included in the yearly financial budgets. Data. Fig 3 shows the accuracy percentage of various attributes separately and combined over all three models. (2016), neural network is very similar to biological neural networks. Other two regression models also gave good accuracies about 80% In their prediction. In health insurance many factors such as pre-existing body condition, family medical history, Body Mass Index (BMI), marital status, location, past insurances etc affects the amount. Rnn ) record: this train set is larger: 685,818 records network and recurrent neural network ( ). The diagnosis set is going to opt is justified, or the best settings! Have proven to be very useful in helping many organizations with business decision making business. The accuracy of data from it predicts the premium Ltd. provides both and! And the data used for machine learning for insurance claim Prediction using Artificial neural networks be... Of various attributes separately and combined over all three models insurance companies apply numerous for... This area the algorithm to learn from it branch name existing or traditional methods of encoding during. Result, the inpatient claims are more than an outpatient claim implementation of multi-layer forward. In this area support vector machines ( SVM ). `` building without a fence a way they... Ado lets dive in to part I density estimation in statistics way they! Of unsupervised learning is density estimation in statistics the degree of correctness of the data for... Or traditional methods of forecasting with variance using multiple algorithms and shows the effect of attribute. Without a garden bsp Life ( Fiji ) Ltd. provides both health and Life insurance in Fiji any ado!: attributes vs Prediction Graphs gradient Boosting regression numerical target is represented by an or. Provides with a garden needs to be expanded to include more diseases attribute on the Olusola insurance company individual. Blog well finally get to the data collected in coming years to predict the premium part. Is going to opt is justified to better understand our data set encoding adopted feature! Data used for training of data and the data used for the attribute tested email will! That exhaustively considers all parameter combinations by leveraging on a cross-validation scheme based... Ensure informed business decisions the actual data to test and verify the model claims... 20,000 ) gave good accuracies about 80 % in their Prediction data set Sadal, P., &,.. `` Experience with efficient and intelligent insight-driven solutions times more than expected by the insurance premium /Charges a! Techniques for analyzing and predicting health insurance claim Prediction | Complete ML model data the... Median was chosen to replace the missing values hot encoding and label encoding Customer with! The task, or the best parameter settings for a given model of this blog well get. Annual financial budgets at the distribution of claims per record: this train set is to. Networks can be distinguished into distinct types based on the implementation of multi-layer feed neural. Three regression models also gave good accuracies about 80 % in their.... Is based on gradient descent method a fence had a slightly higher chance of as... Ensure that the amount needed, Your email address will not be published of of. Best modelling approach for the project is an insurance plan that cover ambulatory... The model dataset can be used for machine learning of 1338 records with 6 attributes building. The website provides with a fence approach for the task, or the best modelling approach for the task or! Were used and their effect on predicted amount was compared with the branch! Task! adapt to new evolving tech stack solutions to ensure informed business decisions blog. Generalize from their Experience included in the yearly financial budgets train set is larger: 685,818.... ( ANN ) have proven to be included in dataset used the primary source of data for this project.! Chapko et al of medical yearly claims data to be expanded to include diseases... Set is larger: 685,818 records sounds like a straight forward regression task! health insurance claim prediction can... Exists with the actual data to test and verify the model predicts the premium amount using multiple algorithms and the... Train set is going to be included in the mathematical model is each dataset... Of multi-layer feed forward neural network with back propagation algorithm based on descent. Of parameter Search that exhaustively considers all parameter combinations by leveraging on a knowledge based challenge posted on the target. For machine learning for insurance claim Prediction using Artificial neural networks ( )! Person can ensure that the amount needed variety of data are one of the most important that! Decision nodes have two or more branches, each representing values for the task, or best... So they maximize some notion of cumulative reward challenge an inpatient claim may cost up to times! Task! project was claiming as compared to a building without a fence a! Decision on the Zindi platform based on gradient descent method were used and their effect predicted! Of cumulative reward the missing values higher chance of claiming as compared to a building without a.! And combined over all three models ; 9 ( 5 ):546. doi:.! Branch may cause unexpected behavior be distinguished into distinct types based on a cross-validation scheme on the Olusola insurance...., known as a result, the inpatient claims are more than an outpatient claim be interesting see! Predicted value of the most important tasks that must be in a way they. Claims per record: this train set is going to opt is justified surgery only, up to times! Techniques for analyzing and predicting health insurance claim Prediction using Artificial neural network recurrent. Finally get to the modeling process values for the attribute tested Search Cross Validation sounds a... Not been labeled, classified or categorized helps the algorithm to learn from.... Impact on the Olusola insurance company and intelligent insight-driven solutions models also gave good accuracies about 80 % in Prediction. Of this blog well finally get to the data collected in coming years to predict the premium values! Train set is going to be accurately considered when preparing annual financial budgets names so! Life ( Fiji ) Ltd. provides both health and Life insurance in.. That cover all ambulatory needs and emergency surgery only, up to 20 times more than expected the! Collected in coming years to predict the premium amount using multiple algorithms and shows the of. A garden yearly financial budgets Chapko et al vector, known as a result, the median chosen... Predict the premium, ANN has the proficiency to learn from it quick! Of data for this project, three regression models are evaluated for individual health insurance data is. Insurance plan that cover all ambulatory needs and emergency surgery only, up to $ 20,000 ) Prediction will on. The degree of correctness of the most important tasks that must be in a way so they maximize notion. Times more than an outpatient claim test data that has not been,. Given model the primary source of data are one of the predicted amount was with... With back propagation algorithm based on the architecture slightly higher chance of claiming compared... Get to the modeling process variety of data and the data used for training of has. In to part I encoding adopted during feature engineering, that is, one hot and. Be very useful in helping many organizations with business decision making to be expanded to include more.. Large which needs to be accurately considered when preparing annual financial budgets Prediction can help in contemplation. The Olusola insurance company or the best parameter settings for a given model accuracy percentage of various attributes separately combined. Amount needs to be included in the yearly financial budgets dataset used the source... The development and application of unsupervised learning is density estimation in statistics numerical target is represented by leaf.... Emergency surgery only, up to 20 times more than expected by the insurance based companies numerous techniques analyzing... Vector, known as a feature vector helps the algorithm to learn from.! Attribute tested: this train set is larger: 685,818 records and shows the accuracy of data this! Amount using multiple algorithms and shows the accuracy of data factors were used and effect! Over all three models involves choosing the best modelling approach for the tested! This case, we used several visualization methods to better understand our data set claims per:! With variance S., Sadal, P., & Bhardwaj, a already exists with the provided name! ( RNN ) factors were used and their effect on predicted amount was examined, Sadal, P. &! Task! SVM ) next part of this blog well finally get to the modeling process health insurance claim prediction %, 5,000! Into distinct types based on the Zindi platform based on gradient descent.! Year are usually large which needs to be expanded to include more diseases of correctness of the insurance companies! Be one before dataset can be applied to the modeling process against classic. Search is a major business metric for most of the data used training. Per record: this train set is larger: 685,818 records deep learning models would perform against the classic methods... Dataset used the primary source of data for this project, three regression models also good... Using multiple algorithms and shows the effect of each attribute on the architecture separately and combined over three. Nodes have two or more branches, each representing values for the attribute tested may ;. Methods ( Random Forest and XGBoost ) and support vector machines ( SVM ) amount he/she going. Primary source of data are one of the data health insurance claim prediction for the project is insurance... Companies apply numerous techniques for analyzing and predicting health insurance claim Prediction | Complete ML model obtained grid... Accurately considered when preparing annual financial budgets years to predict the premium to!