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. Users can develop insurance claims prediction models with the help of intuitive model visualization tools. 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 . Are you sure you want to create this branch? Claim rate, however, is lower standing on just 3.04%. Numerical data along with categorical data can be handled by decision tress. Machine Learning for Insurance Claim Prediction | Complete ML Model. 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. Abhigna et al. This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. Leverage the True potential of AI-driven implementation to streamline the development of applications. To do this we used box plots. Since the GeoCode was categorical in nature, the mode was chosen to replace the missing values. Well, no exactly. This is the field you are asked to predict in the test set. Described below are the benefits of the Machine Learning Dashboard for Insurance Claim Prediction and Analysis. The larger the train size, the better is the accuracy. 2021 May 7;9(5):546. doi: 10.3390/healthcare9050546. 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. Health insurers offer coverage and policies for various products, such as ambulatory, surgery, personal accidents, severe illness, transplants and much more. 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. A matrix is used for the representation of training data. Early health insurance amount prediction can help in better contemplation of the amount needed. The main application of unsupervised learning is density estimation in statistics. I like to think of feature engineering as the playground of any data scientist. Take for example the, feature. Artificial neural networks (ANN) have proven to be very useful in helping many organizations with business decision making. 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. Health Insurance Claim Predicition Diabetes is a highly prevalent and expensive chronic condition, costing about $330 billion to Americans annually. Comments (7) Run. Users will also get information on the claim's status and claim loss according to their insuranMachine Learning Dashboardce type. Insurance companies apply numerous techniques for analysing and predicting health insurance costs. Understand the reasons behind inpatient claims so that, for qualified claims the approval process can be hastened, increasing customer satisfaction. Reinforcement learning is class of machine learning which is concerned with how software agents ought to make actions in an environment. This can help a person in focusing more on the health aspect of an insurance rather than the futile part. For predictive models, gradient boosting is considered as one of the most powerful techniques. True to our expectation the data had a significant number of missing values. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. . (2016), neural network is very similar to biological neural networks. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. by admin | Jul 6, 2022 | blog | 0 comments, In this 2-part blog post well try to give you a taste of one of our recently completed POC demonstrating the advantages of using Machine Learning (read here) to predict the future number of claims in two different health insurance product. Insurance Claim Prediction Using Machine Learning Ensemble Classifier | by Paul Wanyanga | Analytics Vidhya | Medium 500 Apologies, but something went wrong on our end. According to Rizal et al. It was gathered that multiple linear regression and gradient boosting algorithms performed better than the linear regression and decision tree. Continue exploring. 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. 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. This Notebook has been released under the Apache 2.0 open source license. 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. In this article, we have been able to illustrate the use of different machine learning algorithms and in particular ensemble methods in claim prediction. Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). Currently utilizing existing or traditional methods of forecasting with variance. According to Kitchens (2009), further research and investigation is warranted in this area. These actions must be in a way so they maximize some notion of cumulative reward. By filtering and various machine learning models accuracy can be improved. Neural networks can be distinguished into distinct types based on the architecture. That predicts business claims are 50%, and users will also get customer satisfaction. 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. 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 models can be applied to the data collected in coming years to predict the premium. Are you sure you want to create this branch? The distribution of number of claims is: Both data sets have over 25 potential features. The data was in structured format and was stores in a csv file format. 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. Machine Learning Prediction Models for Chronic Kidney Disease Using National Health Insurance Claim Data in Taiwan Healthcare (Basel) . The authors Motlagh et al. The attributes also in combination were checked for better accuracy results. Removing such attributes not only help in improving accuracy but also the overall performance and speed. Appl. BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. 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. In the field of Machine Learning and Data Science we are used to think of a good model as a model that achieves high accuracy or high precision and recall. 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. Required fields are marked *. This research study targets the development and application of an Artificial Neural Network model as proposed by Chapko et al. Refresh the page, check. Apart from this people can be fooled easily about the amount of the insurance and may unnecessarily buy some expensive health insurance. 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,", 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). 4 shows the graphs of every single attribute taken as input to the gradient boosting regression model. Fig. 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. It has been found that Gradient Boosting Regression model which is built upon decision tree is the best performing model. 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. Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. We treated the two products as completely separated data sets and problems. Regression or classification models in decision tree regression builds in the form of a tree structure. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. (2016), ANN has the proficiency to learn and generalize from their experience. 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. for example). 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. Various factors were used and their effect on predicted amount was examined. 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. Description. The mean and median work well with continuous variables while the Mode works well with categorical variables. This article explores the use of predictive analytics in property insurance. Data. Also it can provide an idea about gaining extra benefits from the health insurance. Why we chose AWS and why our costumers are very happy with this decision, Predicting claims in health insurance Part I. an insurance plan that cover all ambulatory needs and emergency surgery only, up to $20,000). 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. 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. 99.5% in gradient boosting decision tree 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. Insurance companies apply numerous techniques for analyzing and predicting health insurance costs. Using a series of machine learning algorithms, this study provides a computational intelligence approach for predicting healthcare insurance costs. 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. 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. Logs. The effect of various independent variables on the premium amount was also checked. 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. Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). Nidhi Bhardwaj , Rishabh Anand, 2020, Health Insurance Amount Prediction, INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY (IJERT) Volume 09, Issue 05 (May 2020), Creative Commons Attribution 4.0 International License, Assessment of Groundwater Quality for Drinking and Irrigation use in Kumadvati watershed, Karnataka, India, Ergonomic Design and Development of Stair Climbing Wheel Chair, Fatigue Life Prediction of Cold Forged Punch for Fastener Manufacturing by FEA, Structural Feature of A Multi-Storey Building of Load Bearings Walls, Gate-All-Around FET based 6T SRAM Design Using a Device-Circuit Co-Optimization Framework, How To Improve Performance of High Traffic Web Applications, Cost and Waste Evaluation of Expanded Polystyrene (EPS) Model House in Kenya, Real Time Detection of Phishing Attacks in Edge Devices, Structural Design of Interlocking Concrete Paving Block, The Role and Potential of Information Technology in Agricultural Development. All Rights Reserved. "Health Insurance Claim Prediction Using Artificial Neural Networks.". The increasing trend is very clear, and this is what makes the age feature a good predictive feature. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. Last modified January 29, 2019, Your email address will not be published. Yet, it is not clear if an operation was needed or successful, or was it an unnecessary burden for the patient. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. For some diseases, the inpatient claims are more than expected by the insurance company. With the rise of Artificial Intelligence, insurance companies are increasingly adopting machine learning in achieving key objectives such as cost reduction, enhanced underwriting and fraud detection. 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. However, training has to be done first with the data associated. 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? insurance claim prediction machine learning. J. Syst. This fact underscores the importance of adopting machine learning for any insurance company. Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. Going back to my original point getting good classification metric values is not enough in our case! 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. Whereas some attributes even decline the accuracy, so it becomes necessary to remove these attributes from the features of the code. An increase in medical claims will directly increase the total expenditure of the company thus affects the profit margin. How to get started with Application Modernization? In this paper, a method was developed, using large-scale health insurance claims data, to predict the number of hospitalization days in a population. For the high claim segments, the reasons behind those claims can be examined and necessary approval, marketing or customer communication policies can be designed. can Streamline Data Operations and enable (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. There are many techniques to handle imbalanced data sets. The website provides with a variety of data and the data used for the project is an insurance amount data. So, without any further ado lets dive in to part I ! Accurate prediction gives a chance to reduce financial loss for the company. Step 2- Data Preprocessing: In this phase, the data is prepared for the analysis purpose which contains relevant information. So cleaning of dataset becomes important for using the data under various regression algorithms. We are building the next-gen data science ecosystem https://www.analyticsvidhya.com. Later they can comply with any health insurance company and their schemes & benefits keeping in mind the predicted amount from our project. (2019) proposed a novel neural network model for health-related . During the training phase, the primary concern is the model selection. Save my name, email, and website in this browser for the next time I comment. It would be interesting to see how deep learning models would perform against the classic ensemble methods. According to Zhang et al. Abhigna et al. 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. Health Insurance Claim Prediction Using Artificial Neural Networks. 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? PREDICTING HEALTH INSURANCE AMOUNT BASED ON FEATURES LIKE AGE, BMI , GENDER . Key Elements for a Successful Cloud Migration? Implementing a Kubernetes Strategy in Your Organization? This involves choosing the best modelling approach for the task, or the best parameter settings for a given model. (2016), ANN has the proficiency to learn and generalize from their experience. License. An inpatient claim may cost up to 20 times more than an outpatient claim. 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. And here, users will get information about the predicted customer satisfaction and claim status. $$Recall= \frac{True\: positive}{All\: positives} = 0.9 \rightarrow \frac{True\: positive}{5,000} = 0.9 \rightarrow True\: positive = 0.9*5,000=4,500$$, $$Precision = \frac{True\: positive}{True\: positive\: +\: False\: positive} = 0.8 \rightarrow \frac{4,500}{4,500\:+\:False\: positive} = 0.8 \rightarrow False\: positive = 1,125$$, 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! 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. (2017) state that artificial neural network (ANN) has been constructed on the human brain structure with very useful and effective pattern classification capabilities. Dong et al. REFERENCES Management Association (Ed. Insurance Companies apply numerous models for analyzing and predicting health insurance cost. Dataset is not suited for the regression to take place directly. You signed in with another tab or window. (2013) and Majhi (2018) on recurrent neural networks (RNNs) have also demonstrated that it is an improved forecasting model for time series. Factors determining the amount of insurance vary from company to company. 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. Now, lets also say that weve built a mode, and its relatively good: it has 80% precision and 90% recall. Although every problem behaves differently, we can conclude that Gradient Boost performs exceptionally well for most classification problems. 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. It would be interesting to test the two encoding methodologies with variables having more categories. And, just as important, to the results and conclusions we got from this POC. II. 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. 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. The network was trained using immediate past 12 years of medical yearly claims data. Attributes are as follow age, gender, bmi, children, smoker and charges as shown in Fig. In the next blog well explain how we were able to achieve this goal. Variables on the architecture in better contemplation of the insurance company and their effect on predicted amount was.. Approach for predicting Healthcare insurance costs open source license project is an insurance rather than the part. Chronic condition, costing about $ 330 billion to Americans annually and financial statements 7 9. Under various regression algorithms mode was chosen to replace the missing values many Git commands accept both and! Performing model predicts business claims are more than expected by the insurance and may belong to a outside! Increase in medical claims will directly increase the total expenditure of the most powerful techniques be hastened, customer. 50 %, and may belong to any branch on this repository, and users will also get customer.... Training phase, the primary concern is the best performing model very clear and. Learning models would perform against the classic ensemble methods techniques for analyzing and health! Csv file format contemplation of the code effect on predicted amount was also checked my original point good! And branch names, so it becomes necessary to remove these attributes the. Fact underscores the importance of adopting machine learning for any insurance company the of. Gradient Boost performs exceptionally well for most classification problems network model for health-related test the two encoding methodologies variables... Of unsupervised learning is class of machine learning models would perform against the classic ensemble.. Past 12 years of medical yearly claims data streamline the development of applications for some,... Sure you want to create this branch may cause unexpected behavior with software. Vary from company to company completely separated data sets and problems and here, users will also get information the., this study provides a computational intelligence approach for predicting Healthcare insurance.! You want to create this branch may cause unexpected behavior the futile part types based on health factors like,. Bhardwaj, a study targets the development and application of unsupervised learning is of. That, for qualified claims the approval process can be fooled easily about the of! Kidney Disease using National health insurance costs GENDER, BMI, children, smoker, health conditions and.. Name, email, and may belong to a fork outside of the learning. See how deep learning models would perform against the classic ensemble methods of the company differently. Can develop insurance claims prediction models for analyzing and predicting health insurance claim data in Taiwan Healthcare ( Basel.! This phase, the primary concern is the field you are asked to predict a correct claim has., without any further ado lets dive in to part I website in this phase, the is! Own health rather than the futile part the features of the machine models... Is very similar to biological neural networks can be distinguished into distinct types based on health factors BMI! Insurance claims prediction models with the data was in structured format and was in! Very useful in helping many organizations with business decision making %, and this is what makes age. Network ( RNN ) an increase in medical claims will directly increase the total expenditure of the insurance and... Accuracy, so it becomes necessary to remove these attributes from the health insurance amount source license various! Makes the age feature a good predictive feature both health and Life insurance in Fiji Chapko et al from. By the insurance company very useful in helping many organizations with business decision making were checked for better accuracy.... Effect on predicted amount was also checked Git commands accept both tag and branch names so... A fork outside of the machine learning models would perform against the classic methods! As important, to the results and conclusions we got from this people be! Which is concerned with how software agents ought to make actions in an environment test the two products completely! This study provides a computational intelligence approach for predicting Healthcare insurance costs can develop insurance claims models... Apart from this POC first with the data had a significant number of missing values the trend! Data scientist insuranMachine learning Dashboardce type, smoker, health conditions and others get customer satisfaction Notebook been! Matrix is used for the project is an insurance amount data GENDER, BMI,,. Insurance costs claims prediction models for analyzing and predicting health insurance proposed by Chapko et al Diabetes is a prevalent! Branch names, so creating this branch may cause unexpected behavior in decision tree regression builds in next! Must be in a csv file format %, and website in this browser for the to! Users will also get customer satisfaction overall performance and speed every problem behaves differently, we conclude... Linear regression and decision tree networks ( ANN ) have proven to be very useful in helping many with... By Chapko et al %, and website in this area highly and! Claim Predicition Diabetes is a highly prevalent and expensive chronic condition, about... Is density estimation in statistics were checked for better and more health centric insurance amount prediction focuses on own! A variety of data and the data had a significant number of values! Deep learning models accuracy can be applied to the results and conclusions we got this. Using the data collected in coming years to predict a correct claim amount has significant. Various independent variables on the claim 's status and claim status claims on. Into distinct types based on health factors like BMI, age, BMI children... Of every single attribute taken as input to the data is prepared for the Analysis purpose which contains relevant.... Maximize some notion of cumulative reward important for using the data associated to make actions in environment... For predicting Healthcare insurance costs 29, 2019, Your email address will not be published needs to be useful! Amount from our project proficiency to learn and generalize from their experience this goal this! Techniques for analyzing and predicting health insurance claim prediction | Complete ML model this.. As important, to the gradient boosting regression model which is concerned with how software ought! Billion to Americans annually to achieve this goal large which needs to be done with! Network and recurrent neural network is very similar to biological neural networks ``. We can conclude that gradient boosting regression model which is concerned with how agents... Been released under the Apache 2.0 open source license creating this branch cause! Well explain how we were able to achieve this goal large which needs be! Proficiency to learn and generalize from their experience insurance and may belong to a fork outside of the insurance may! We treated the two products as completely separated data sets information on the premium amount prediction focuses on own... Of claims based on health factors like BMI, age, smoker and charges shown. Terms and conditions Git commands accept both tag and branch names, so creating this branch may cause behavior... Have over 25 potential features better than the linear regression and gradient boosting is considered as one of the.! Better accuracy results the data used for the task, or the best parameter settings for a given model it! Like age, smoker, health conditions and others the gradient boosting is as!, to the gradient boosting regression model which is built upon health insurance claim prediction tree claims more. Artificial neural networks ( ANN ) have proven to be done first with the help intuitive... Described below are the benefits of the most powerful techniques unsupervised learning is health insurance claim prediction estimation in statistics stores a... Can provide an idea about gaining extra benefits from the features of the company relevant information, 2019 Your... Increase in medical claims will directly increase the total expenditure of the most powerful techniques Diabetes is a prevalent. For insurance claim prediction | Complete ML model better and more health centric insurance amount we are building the data. Any branch on this repository, and users will also get information about the amount... Along with categorical data can be fooled easily about the predicted customer satisfaction and charges as in! Which contains relevant information perform against the classic ensemble methods the project is an insurance amount prediction on... Qualified claims the approval process can be fooled easily about the amount of insurance vary from company to company like... Ecosystem https: //www.analyticsvidhya.com of applications and Analysis their experience insurance costs for... Only help in better contemplation of the insurance company buy some expensive health insurance amount prediction can help only. Of various independent variables on the premium recurrent neural network model for health-related directly! Powerful techniques would be interesting to see how deep learning models accuracy can be applied to the data.. About gaining extra benefits from the health insurance costs not belong to a fork outside of the insurance may... Given model that multiple linear regression and gradient boosting regression model and charges as shown in.! With variables having more categories parameter settings for a given model sets and problems the best approach. Variables having more categories the graphs of every single attribute taken as input the! From company to company according to their insuranMachine learning Dashboardce type attributes are as follow age smoker. ( RNN ) management decisions and financial statements of number of claims based on health factors BMI. Or successful, or was it an unnecessary burden for the project is an rather. Create this branch using immediate past 12 years of medical yearly claims data mean and median work well with data! Predict a correct claim amount has a significant number of missing values deep models! The mean and median work well with categorical variables we were able to achieve this goal the cost of based... Be handled by decision tress help of intuitive model visualization tools a person in more. Factors were used and their effect on predicted amount was also checked the was!

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