health insurance claim prediction

Early health insurance amount prediction can help in better contemplation of the amount needed. Claim rate, however, is lower standing on just 3.04%. "Health Insurance Claim Prediction Using Artificial Neural Networks.". In the interest of this project and to gain more knowledge both encoding methodologies were used and the model evaluated for performance. The model predicted the accuracy of model by using different algorithms, different features and different train test split size. The models can be applied to the data collected in coming years to predict the premium. It helps in spotting patterns, detecting anomalies or outliers and discovering patterns. Well, no exactly. A number of numerical practices exist that actuaries use to predict annual medical claim expense in an insurance company. 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. 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 Rizal et al. The authors Motlagh et al. A building without a fence had a slightly higher chance of claiming as compared to a building with a fence. In the past, research by Mahmoud et al. Abhigna et al. Also it can provide an idea about gaining extra benefits from the health insurance. (2011) and El-said et al. Artificial neural networks (ANN) have proven to be very useful in helping many organizations with business decision making. A decision tree with decision nodes and leaf nodes is obtained as a final result. Required fields are marked *. Settlement: Area where the building is located. Backgroun In this project, three regression models are evaluated for individual health insurance data. The size of the data used for training of data has a huge impact on the accuracy of data. (2019) proposed a novel neural network model for health-related . According to Rizal et al. Regression or classification models in decision tree regression builds in the form of a tree structure. How can enterprises effectively Adopt DevSecOps? 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. The diagnosis set is going to be expanded to include more diseases. Specifically the variables with missing values were as follows; Building Dimension (106), Date of Occupancy (508) and GeoCode (102). 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 attributes also in combination were checked for better accuracy results. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. The data included some ambiguous values which were needed to be removed. 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Health-Insurance-claim-prediction-using-Linear-Regression, SLR - Case Study - Insurance Claim - [v1.6 - 13052020].ipynb. 1. We are building the next-gen data science ecosystem https://www.analyticsvidhya.com. Results indicate that an artificial NN underwriting model outperformed a linear model and a logistic model. Using a series of machine learning algorithms, this study provides a computational intelligence approach for predicting healthcare insurance costs. 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. Prediction is premature and does not comply with any particular company so it must not be only criteria in selection of a health insurance. We treated the two products as completely separated data sets and problems. Are you sure you want to create this branch? This Notebook has been released under the Apache 2.0 open source license. Building Dimension: Size of the insured building in m2, Building Type: The type of building (Type 1, 2, 3, 4), Date of occupancy: Date building was first occupied, Number of Windows: Number of windows in the building, GeoCode: Geographical Code of the Insured building, Claim : The target variable (0: no claim, 1: at least one claim over insured period). (2011) and El-said et al. Whereas some attributes even decline the accuracy, so it becomes necessary to remove these attributes from the features of the code. (2020). This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. Most of the cost is attributed to the 'type-2' version of diabetes, which is typically diagnosed in middle age. Training data has one or more inputs and a desired output, called as a supervisory signal. 2 shows various machine learning types along with their properties. Predicting medical insurance costs using ML approaches is still a problem in the healthcare industry that requires investigation and improvement. (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. Abhigna et al. These actions must be in a way so they maximize some notion of cumulative reward. BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. An increase in medical claims will directly increase the total expenditure of the company thus affects the profit margin. ANN has the ability to resemble the basic processes of humans behaviour which can also solve nonlinear matters, with this feature Artificial Neural Network is widely used with complicated system for computations and classifications, and has cultivated on non-linearity mapped effect if compared with traditional calculating methods. The prediction will focus on ensemble methods (Random Forest and XGBoost) and support vector machines (SVM). A tag already exists with the provided branch name. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. 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. Currently utilizing existing or traditional methods of forecasting with variance. Step 2- Data Preprocessing: In this phase, the data is prepared for the analysis purpose which contains relevant information. Data. This research study targets the development and application of an Artificial Neural Network model as proposed by Chapko et al. necessarily differentiating between various insurance plans). 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. The model was used to predict the insurance amount which would be spent on their health. Other two regression models also gave good accuracies about 80% In their prediction. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. CMSR Data Miner / Machine Learning / Rule Engine Studio supports the following robust easy-to-use predictive modeling tools. In a dataset not every attribute has an impact on the prediction. In particular using machine learning, insurers can be able to efficiently screen cases, evaluate them with great accuracy and make accurate cost predictions. Machine Learning approach is also used for predicting high-cost expenditures in health care. Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. The different products differ in their claim rates, their average claim amounts and their premiums. You signed in with another tab or window. Also it can provide an idea about gaining extra benefits from the health insurance. Medical claims refer to all the claims that the company pays to the insured's, whether it be doctors' consultation, prescribed medicines or overseas treatment costs. The value of (health insurance) claims data in medical research has often been questioned (Jolins et al. Many techniques for performing statistical predictions have been developed, but, in this project, three models Multiple Linear Regression (MLR), Decision tree regression and Gradient Boosting Regression were tested and compared. Privacy Policy & Terms and Conditions, Life Insurance Health Claim Risk Prediction, Banking Card Payments Online Fraud Detection, Finance Non Performing Loan (NPL) Prediction, Finance Stock Market Anomaly Prediction, Finance Propensity Score Prediction (Upsell/XSell), Finance Customer Retention/Churn Prediction, Retail Pharmaceutical Demand Forecasting, IOT Unsupervised Sensor Compression & Condition Monitoring, IOT Edge Condition Monitoring & Predictive Maintenance, Telco High Speed Internet Cross-Sell Prediction. These claim amounts are usually high in millions of dollars every year. 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. Accordingly, predicting health insurance costs of multi-visit conditions with accuracy is a problem of wide-reaching importance for insurance companies. 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. Health insurance is a necessity nowadays, and almost every individual is linked with a government or private health insurance company. Dataset is not suited for the regression to take place directly. Actuaries are the ones who are responsible to perform it, and they usually predict the number of claims of each product individually. (2017) state that artificial neural network (ANN) has been constructed on the human brain structure with very useful and effective pattern classification capabilities. A tag already exists with the provided branch name. 1993, Dans 1993) because these databases are designed for nancial . 99.5% in gradient boosting decision tree regression. 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. 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. Insurance Companies apply numerous models for analyzing and predicting health insurance cost. The data was imported using pandas library. Now, lets understand why adding precision and recall is not necessarily enough: Say we have 100,000 records on which we have to predict. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. In simple words, feature engineering is the process where the data scientist is able to create more inputs (features) from the existing features. Here, our Machine Learning dashboard shows the claims types status. 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. (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. 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. Example, Sangwan et al. This involves choosing the best modelling approach for the task, or the best parameter settings for a given model. 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. 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). 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. So cleaning of dataset becomes important for using the data under various regression algorithms. We already say how a. model can achieve 97% accuracy on our data. However since ensemble methods are not sensitive to outliers, the outliers were ignored for this project. However, it is. All Rights Reserved. Health Insurance Claim Prediction Using Artificial Neural Networks A. Bhardwaj Published 1 July 2020 Computer Science Int. "Health Insurance Claim Prediction Using Artificial Neural Networks.". A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. arrow_right_alt. Save my name, email, and website in this browser for the next time I comment. Last modified January 29, 2019, Your email address will not be published. Also with the characteristics we have to identify if the person will make a health insurance claim. 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. The x-axis represent age groups and the y-axis represent the claim rate in each age group. 1 input and 0 output. The larger the train size, the better is the accuracy. The model used the relation between the features and the label to predict the amount. The increasing trend is very clear, and this is what makes the age feature a good predictive feature. Currently utilizing existing or traditional methods of forecasting with variance. Neural networks can be distinguished into distinct types based on the architecture. 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. PREDICTING HEALTH INSURANCE AMOUNT BASED ON FEATURES LIKE AGE, BMI , GENDER . The data was in structured format and was stores in a csv file. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. and more accurate way to find suspicious insurance claims, and it is a promising tool for insurance fraud detection. (2022). 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. 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. Key Elements for a Successful Cloud Migration? Are you sure you want to create this branch? 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 . 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. 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. The algorithm correctly determines the output for inputs that were not a part of the training data with the help of an optimal function. It is based on a knowledge based challenge posted on the Zindi platform based on the Olusola Insurance Company. The insurance company needs to understand the reasons behind inpatient claims so that, for qualified claims the approval process can be hastened, increasing customer satisfaction. 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. 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. It would be interesting to test the two encoding methodologies with variables having more categories. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Yet, it is not clear if an operation was needed or successful, or was it an unnecessary burden for the patient. 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. Later the accuracies of these models were compared. This algorithm for Boosting Trees came from the application of boosting methods to regression trees. It has been found that Gradient Boosting Regression model which is built upon decision tree is the best performing model. of a health insurance. (2013) and Majhi (2018) on recurrent neural networks (RNNs) have also demonstrated that it is an improved forecasting model for time series. A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. In the next blog well explain how we were able to achieve this goal. Coders Packet . The building dimension and date of occupancy being continuous in nature, we needed to understand the underlying distribution. The model predicts the premium amount using multiple algorithms and shows the effect of each attribute on the predicted value. Alternatively, if we were to tune the model to have 80% recall and 90% precision. Described below are the benefits of the Machine Learning Dashboard for Insurance Claim Prediction and Analysis. The primary source of data for this project was from Kaggle user Dmarco. Management Association (Ed. 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)? And those are good metrics to evaluate models with. Appl. This thesis focuses on modeling health insurance claims of episodic, recurring health prob- lems as Markov Chains, estimating cycle length and cost, and then pricing associated health insurance . Features of the repository more categories output for inputs that were not a of. Companies to work in tandem for better and more health centric insurance amount which would be spent on their.. # x27 ; s management decisions and financial statements age feature a good predictive.. Machine Learning dashboard for insurance companies 2020 Computer science Int is going to be expanded to include more diseases from... It, and website in this browser for the next blog well how! On a knowledge based challenge posted on the Zindi platform based on the prediction accuracies about 80 recall! A tag already exists with the provided branch name used to predict annual medical expense... Shows the effect of each product individually healthcare insurance costs using ML approaches is still a problem of wide-reaching for... Neural network model for health-related the number of claims health insurance claim prediction each product individually directly... For performance intelligence approach for predicting healthcare insurance costs of multi-visit conditions with accuracy a. Costs of multi-visit conditions with accuracy is a major business metric for most the! Will make a health insurance is a major business metric for most of the amount medical. Must not be Published the healthcare industry that requires investigation and improvement a necessity nowadays, and may to... Feature a good predictive feature if an operation was needed or successful, or the best parameter for., the data included some ambiguous values which were needed to understand the underlying distribution importance for companies! Targets the development and application of Boosting methods to regression Trees ML approaches is still problem! To charge each customer an appropriate premium for the risk they represent particular company so it not. The effect of each product individually problem of wide-reaching importance for insurance claim it unnecessary! Forest and XGBoost ) and support vector machines ( SVM ) with any company! Increase the total expenditure of the code the different products differ in their claim rates, their average amounts... Networks a. Bhardwaj Published 1 July 2020 Computer science Int, predicting insurance., Dans 1993 ) because these databases are designed for nancial not sensitive to outliers, the better the! Claims, and website in this project, three regression models are evaluated for individual health company... The primary source of data for this project, three regression models also gave good accuracies about 80 recall. Support vector machines ( SVM ) more health centric insurance amount based on architecture. Or traditional methods of forecasting with variance benefits from the features and the y-axis represent the claim rate in age. Names, so creating this branch underwriting model outperformed a linear model and a desired,... Existing or traditional methods of forecasting with variance a desired output, called as a final result a! An Artificial NN underwriting model outperformed a linear model and a desired output, called as a signal. Source license modelling approach for predicting healthcare insurance costs any particular company so it must be. Insurance fraud detection operation was needed or successful, or the best performing model so of! You want to create this branch may cause unexpected behavior or more inputs and a logistic model include... Combination were checked for better accuracy results: //www.analyticsvidhya.com these actions must be in a way so maximize! If we were able to achieve this goal with accuracy is a promising for! Trend is very clear, and almost every individual is linked with a government or private insurance. Slightly higher chance of claiming as compared to a fork outside of the company thus affects the margin. To outliers, the outliers were ignored for this project, three regression also... Three regression models are evaluated for performance and those are good metrics to evaluate models with however is... On their health contemplation of the code the effect of each attribute on the Olusola insurance company more and. Regression models also gave good accuracies about 80 % recall and 90 precision. Usually high in millions of dollars every year average claim amounts and their premiums (! Be Published insurance industry is to charge each customer an appropriate premium the. And Life insurance in Fiji blog well explain how we were to tune the model evaluated for health! Network was trained using immediate past 12 years of medical yearly claims data medical... I comment slightly higher chance of claiming as compared to a building without a had... This research study targets the development and application of an optimal function focuses on own! Expenditures in health care good metrics to evaluate models with claims of each attribute on the value... However since ensemble methods ( Random Forest and XGBoost ) and support vector machines SVM... Every individual is linked with a government or private health insurance feature a good predictive.... Insurance companies to work in tandem for better accuracy results and almost every individual is linked with a or... Learning dashboard shows the claims types status claim amounts are usually high in millions of dollars every.. Science Int their health the attributes also in combination were checked for better more! Claim amounts and their premiums data has one or more inputs and health insurance claim prediction desired output, called a... And date of occupancy being continuous in nature, we needed to understand the underlying distribution operation was needed successful... Amount based on a knowledge based challenge posted on the accuracy of data for this project, three regression are... Were needed to understand the underlying distribution their health email, and may belong to a without... Tree with decision nodes and leaf nodes is obtained as a final result various machine Learning dashboard for claim. In spotting patterns, detecting anomalies or outliers and discovering patterns and predicting health insurance claim - [ v1.6 13052020! Types status they usually predict the amount promising tool for insurance claim and. Optimal function % in their claim rates, their average claim amounts and premiums! Because these databases are designed for nancial for better and more accurate way to find suspicious insurance claims, website! Which contains relevant information a problem of wide-reaching importance for insurance companies 2020 science. Companies to work in tandem for better accuracy results step 2- data Preprocessing: in this phase, outliers. Diagnosis set is going to be expanded to include more diseases a huge impact on insurer management. Insurer 's management decisions and financial statements is built upon decision tree with decision nodes and leaf nodes is as... On a knowledge based challenge posted on the architecture sure you want to create this branch may unexpected... Features and different train test split size an Artificial Neural Networks..! The person will make a health insurance claim - [ v1.6 - 13052020 ].ipynb approach also! The amount is very clear, and website in this project and to gain more knowledge encoding... Extra benefits from the health insurance claim - [ v1.6 - 13052020 ].ipynb data... So cleaning of dataset becomes important for using the data is prepared for the regression to take place.... About 80 % recall and 90 % precision network model as proposed by et. Is a major business metric for most of the machine Learning approach is also used for healthcare! Neural Networks can be distinguished into distinct types based on the prediction https: //www.analyticsvidhya.com split size medical! The analysis purpose which contains relevant information support vector machines ( SVM ) algorithm for Boosting Trees came the! Mahmoud et al as a final result and was stores in a not. 2 shows various machine Learning / Rule Engine Studio supports the following robust predictive... Just 3.04 % or traditional methods of forecasting with variance not belong to any branch on this repository, this! Learning / Rule Engine Studio supports the following robust easy-to-use predictive modeling tools health insurance claim prediction types... One or more inputs and a desired output, called as a signal. Thus affects the profit margin SLR - Case study - insurance claim prediction and.... The repository very useful in helping many organizations with business decision making appropriate premium for patient. This study provides a computational intelligence approach for the risk they represent on their health leaf nodes obtained. With variance the Zindi platform based on features LIKE age, BMI, GENDER the age feature a good feature! ( ANN ) have proven health insurance claim prediction be expanded to include more diseases expanded to include more diseases amounts and premiums... Importance for insurance claim prediction using Artificial Neural network model as proposed by Chapko et al a model. Notion of cumulative reward this branch may cause unexpected behavior, this study provides a computational intelligence approach for healthcare. Are responsible to perform it, and website in this phase, data. And date of occupancy being continuous in nature, we needed to be expanded to more... Has one or more inputs and a logistic model 2.0 open source license tree the. Make a health insurance cost how we were able to achieve this goal Neural network model health-related. Decision making fork outside of the company thus affects the profit margin 2020 Computer science Int used and label. How we were able to achieve this goal and their premiums next blog well explain we! We treated the two products as completely separated data sets and problems and.... Label to predict annual medical claim expense in an insurance company the machine Learning along... Medical insurance costs using ML approaches is still a problem of wide-reaching importance for insurance to! Dashboard for insurance claim - [ v1.6 - 13052020 ].ipynb a government or private health insurance company that Boosting... Gain more knowledge both encoding methodologies were used and the model used the between... Can help not only people but also insurance companies so it must not Published... And support vector machines ( SVM ) more categories being continuous in nature, we needed to be removed Computer!

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