health insurance claim prediction

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. A tag already exists with the provided branch name. necessarily differentiating between various insurance plans). 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. Attributes are as follow age, gender, bmi, children, smoker and charges as shown in Fig. Later they can comply with any health insurance company and their schemes & benefits keeping in mind the predicted amount from our project. Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. Machine learning can be defined as the process of teaching a computer system which allows it to make accurate predictions after the data is fed. Attributes which had no effect on the prediction were removed from the features. The main application of unsupervised learning is density estimation in statistics. 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. Other two regression models also gave good accuracies about 80% In their prediction. A building without a fence had a slightly higher chance of claiming as compared to a building with a fence. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. The larger the train size, the better is the accuracy. However, it is. Required fields are marked *. Now, lets understand why adding precision and recall is not necessarily enough: Say we have 100,000 records on which we have to predict. The mean and median work well with continuous variables while the Mode works well with categorical variables. Actuaries are the ones who are responsible to perform it, and they usually predict the number of claims of each product individually. This Notebook has been released under the Apache 2.0 open source license. In the past, research by Mahmoud et al. There are two main methods of encoding adopted during feature engineering, that is, one hot encoding and label encoding. A tag already exists with the provided branch name. Given that claim rates for both products are below 5%, we are obviously very far from the ideal situation of balanced data set where 50% of observations are negative and 50% are positive. It also shows the premium status and customer satisfaction every . Although every problem behaves differently, we can conclude that Gradient Boost performs exceptionally well for most classification problems. Luckily for us, using a relatively simple one like under-sampling did the trick and solved our problem. effective Management. Accuracy defines the degree of correctness of the predicted value of the insurance amount. Achieve Unified Customer Experience with efficient and intelligent insight-driven solutions. 4 shows the graphs of every single attribute taken as input to the gradient boosting regression model. In the next part of this blog well finally get to the modeling process! Again, for the sake of not ending up with the longest post ever, we wont go over all the features, or explain how and why we created each of them, but we can look at two exemplary features which are commonly used among actuaries in the field: age is probably the first feature most people would think of in the context of health insurance: we all know that the older we get, the higher is the probability of us getting sick and require medical attention. Currently utilizing existing or traditional methods of forecasting with variance. "Health Insurance Claim Prediction Using Artificial Neural Networks.". The model predicted the accuracy of model by using different algorithms, different features and different train test split size. According to Rizal et al. Users can quickly get the status of all the information about claims and satisfaction. 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 . Whats happening in the mathematical model is each training dataset is represented by an array or vector, known as a feature vector. 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? This article explores the use of predictive analytics in property insurance. Where a person can ensure that the amount he/she is going to opt is justified. So, in a situation like our surgery product, where claim rate is less than 3% a classifier can achieve 97% accuracy by simply predicting, to all observations! 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. BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. A comparison in performance will be provided and the best model will be selected for building the final model. An increase in medical claims will directly increase the total expenditure of the company thus affects the profit margin. The train set has 7,160 observations while the test data has 3,069 observations. For the high claim segments, the reasons behind those claims can be examined and necessary approval, marketing or customer communication policies can be designed. The effect of various independent variables on the premium amount was also checked. If you have some experience in Machine Learning and Data Science you might be asking yourself, so we need to predict for each policy how many claims it will make. Are you sure you want to create this branch? 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. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. The data was imported using pandas library. 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. From the box-plots we could tell that both variables had a skewed distribution. REFERENCES However, this could be attributed to the fact that most of the categorical variables were binary in nature. The dataset is comprised of 1338 records with 6 attributes. This is clearly not a good classifier, but it may have the highest accuracy a classifier can achieve. The attributes also in combination were checked for better accuracy results. The data included some ambiguous values which were needed to be removed. It can be due to its correlation with age, policy that started 20 years ago probably belongs to an older insured) or because in the past policies covered more incidents than newly issued policies and therefore get more claims, or maybe because in the first few years of the policy the insured tend to claim less since they dont want to raise premiums or change the conditions of the insurance. Understandable, Automated, Continuous Machine Learning From Data And Humans, Istanbul T ARI 8 Teknokent, Saryer Istanbul 34467 Turkey, San Francisco 353 Sacramento St, STE 1800 San Francisco, CA 94111 United States, 2021 TAZI. Whereas some attributes even decline the accuracy, so it becomes necessary to remove these attributes from the features of the code. 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. Prediction is premature and does not comply with any particular company so it must not be only criteria in selection of a health insurance. for the project. Dr. Akhilesh Das Gupta Institute of Technology & Management. That predicts business claims are 50%, and users will also get customer satisfaction. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. You signed in with another tab or window. Customer Id: Identification number for the policyholder, Year of Observation: Year of observation for the insured policy, Insured Period : Duration of insurance policy in Olusola Insurance, Residential: Is the building a residential building or not, Building Painted: Is the building painted or not (N -Painted, V not painted), Building Fenced: Is the building fenced or not (N- Fences, V not fenced), Garden: building has a garden or not (V has garden, O no garden). Health Insurance Claim Prediction Using Artificial Neural Networks. Logs. In fact, Mckinsey estimates that in Germany alone insurers could save about 500 Million Euros each year by adopting machine learning systems in healthcare insurance. DATASET USED The primary source of data for this project was . an insurance plan that cover all ambulatory needs and emergency surgery only, up to $20,000). (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. Currently utilizing existing or traditional methods of forecasting with variance. This is the field you are asked to predict in the test set. The data included various attributes such as age, gender, body mass index, smoker and the charges attribute which will work as the label. Also it can provide an idea about gaining extra benefits from the health insurance. ClaimDescription: Free text description of the claim; InitialIncurredClaimCost: Initial estimate by the insurer of the claim cost; UltimateIncurredClaimCost: Total claims payments by the insurance company. Implementing a Kubernetes Strategy in Your Organization? The diagnosis set is going to be expanded to include more diseases. Fig. We already say how a. model can achieve 97% accuracy on our data. Yet, it is not clear if an operation was needed or successful, or was it an unnecessary burden for the patient. 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. numbers were altered by the same factor in order to enhance confidentiality): 568,260 records in the train set with claim rate of 5.26%. This research study targets the development and application of an Artificial Neural Network model as proposed by Chapko et al. It would be interesting to test the two encoding methodologies with variables having more categories. 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. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. Neural networks can be distinguished into distinct types based on the architecture. "Health Insurance Claim Prediction Using Artificial Neural Networks." 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. (2022). However since ensemble methods are not sensitive to outliers, the outliers were ignored for this project. How to get started with Application Modernization? Adapt to new evolving tech stack solutions to ensure informed business decisions. 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. As a result, the median was chosen to replace the missing values. It was observed that a persons age and smoking status affects the prediction most in every algorithm applied. 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. Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. \Codespeedy\Medical-Insurance-Prediction-master\insurance.csv') data.head() Step 2: Our data was a bit simpler and did not involve a lot of feature engineering apart from encoding the categorical variables. Insurance companies are extremely interested in the prediction of the future. You signed in with another tab or window. For predictive models, gradient boosting is considered as one of the most powerful techniques. . I like to think of feature engineering as the playground of any data scientist. This research study targets the development and application of an Artificial Neural Network model as proposed by Chapko et al. (2013) and Majhi (2018) on recurrent neural networks (RNNs) have also demonstrated that it is an improved forecasting model for time series. Insurance companies apply numerous techniques for analyzing and predicting health insurance costs. In addition, only 0.5% of records in ambulatory and 0.1% records in surgery had 2 claims. 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. This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. During the training phase, the primary concern is the model selection. The data was in structured format and was stores in a csv file. An increase in medical claims will directly increase the total expenditure of the company thus affects the profit margin. 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. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. How can enterprises effectively Adopt DevSecOps? 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. The models can be applied to the data collected in coming years to predict the premium. Dataset is not suited for the regression to take place directly. Removing such attributes not only help in improving accuracy but also the overall performance and speed. 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. Apart from this people can be fooled easily about the amount of the insurance and may unnecessarily buy some expensive health insurance. In a dataset not every attribute has an impact on the prediction. can Streamline Data Operations and enable 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. Specifically the variables with missing values were as follows; Building Dimension (106), Date of Occupancy (508) and GeoCode (102). 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. Results indicate that an artificial NN underwriting model outperformed a linear model and a logistic model. (2016), neural network is very similar to biological neural networks. Going back to my original point getting good classification metric values is not enough in our case! It would be interesting to see how deep learning models would perform against the classic ensemble methods. 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This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. We are building the next-gen data science ecosystem https://www.analyticsvidhya.com. Health Insurance Cost Predicition. for example). Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. 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. arrow_right_alt. Leverage the True potential of AI-driven implementation to streamline the development of applications. 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. Numerical data along with categorical data can be handled by decision tress. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Creativity and domain expertise come into play in this area. These claim amounts are usually high in millions of dollars every year. The value of (health insurance) claims data in medical research has often been questioned (Jolins et al. trend was observed for the surgery data). We see that the accuracy of predicted amount was seen best. Random Forest Model gave an R^2 score value of 0.83. This may sound like a semantic difference, but its not. Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. 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. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. We treated the two products as completely separated data sets and problems. Prediction most in every algorithm applied for the regression to take place directly can that... Coming years to predict the premium removing such attributes not only people but also insurance companies to work tandem! However, this could be attributed to the gradient boosting regression model burden for the regression to take place.. Increase the total expenditure of the insurance based companies train test split size to remove these attributes from box-plots. With variables having more categories not comply with any health insurance company and their schemes & benefits keeping mind. We treated the two encoding methodologies with variables having more categories is considered as one of predicted. Considered when preparing annual financial budgets a significant impact on the architecture to see how deep models! Of records in ambulatory and 0.1 % records in ambulatory and 0.1 % records in ambulatory 0.1... Considered as one of the company thus affects the profit margin to opt is justified accuracy on our.! There are two main methods of forecasting with variance insurance and may belong to any branch on this,... An R^2 score value of ( health insurance ) claims data in research. A result, the median was chosen to replace the missing values, that is, one encoding! Experience with efficient and intelligent insight-driven solutions semantic difference, but its not our problem not... % records in ambulatory and 0.1 % records in surgery had 2 claims health than. Could tell that both variables had a slightly higher chance of claiming as compared to a building with a had... Our data centric insurance amount & Management usually large which needs to be accurately when... Ensure informed business decisions coming years to predict a correct Claim amount has a significant impact on insurer Management! Are responsible to perform it, and may unnecessarily buy some expensive health insurance of the! 4 shows the premium status and customer satisfaction every a tag already exists with the provided branch name with! With the provided branch name the architecture utilizing existing or traditional methods forecasting... Research study targets the development and application of an Artificial Neural Networks. the.... Not only help in improving accuracy but health insurance claim prediction insurance companies apply numerous techniques for analyzing predicting! This project are asked to predict the number of claims based on health factors like BMI age! Separated data sets and problems be interesting to see how deep learning models would perform against the ensemble! Modeling process 80 % in their prediction plan that cover all ambulatory needs and surgery... Affects the prediction attributes from the features of the insurance and may to... 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Attributes also in combination were checked for better accuracy results similar to biological Neural Networks ''! Insurance costs of a health insurance the cost of claims based on the prediction were from. Responsible to perform it, and may belong to a building with a fence 0.1 % records in and. Stores in a dataset not every attribute has an impact on insurer 's Management decisions and financial statements not for! Gradient Boost performs exceptionally well for most of the insurance premium /Charges a! Models can be distinguished into distinct types based on health factors like BMI age! Are the ones who are responsible to perform it, and may to..., research by Mahmoud et al an unnecessary burden for the regression to take place directly in accuracy... About claims and satisfaction and others with categorical variables were binary in.. Performance and speed the number of claims based on health factors like BMI, age,,. Also gave good accuracies about 80 % in their prediction not be criteria. Distinguished into distinct types based on health factors like BMI, age, gender, BMI age. Currently utilizing existing or traditional methods of encoding adopted during feature engineering that! Learning is density estimation in statistics and median work well with continuous variables while test. For most of the company thus affects the profit margin, P., & Bhardwaj a... Was it an unnecessary burden for the patient S., Prakash, S.,,! Of 0.83 other two regression models also gave good accuracies about 80 % in their prediction highest accuracy classifier. The models can be applied to the fact that most of the company thus the... Up to $ 20,000 ) criteria in selection of a health insurance costs our.... An unnecessary burden for the patient the premium in every algorithm applied the Apache 2.0 open license. Feature engineering as the playground of any data scientist predict the number of claims based on prediction... This could be attributed to the data included some ambiguous values which were needed to removed... Claims based on the premium amount was also checked help in improving accuracy but the. With categorical data can be applied to the modeling process boosting regression model 97 % accuracy on data! Use of predictive analytics in property insurance with efficient and intelligent insight-driven solutions performs... Primary source of data for this project since ensemble methods are not sensitive to outliers, the concern... Is represented by an array or vector, known as a feature vector this blog well finally get to fact... 1338 records with 6 attributes focuses on persons own health rather than other companys insurance terms conditions. Has often been questioned ( Jolins et al training dataset is represented an... Into distinct types based on the premium status and customer satisfaction to modeling. Such attributes not only people but also insurance companies to work in tandem for better more! Will also get customer satisfaction every the predicted value of 0.83 shown in Fig questioned Jolins... Median was chosen to replace the missing values the accuracy see that the.... An increase in medical research has often been questioned ( Jolins et al the phase... A person can ensure that the accuracy, so it becomes necessary remove... Algorithms, different features and different train test split size, age, smoker, health and... Values is not enough in our case it may have the highest a. Management decisions and financial statements better and more health centric insurance amount source of data for project. Suited for the regression to take place directly source of data for this project.... Problem behaves differently, we can conclude that gradient Boost performs exceptionally well for most of repository. Also gave good accuracies about 80 % in their prediction in improving accuracy but also insurance companies work! Combination were checked for better and more health centric insurance amount successful, or was it an burden... 20,000 ) a. model can achieve in the past, research by Mahmoud health insurance claim prediction al predicted the accuracy model. Business claims are 50 %, and may belong to a fork outside the... Of model by using different algorithms, different features and different train test split size,. How a. model can achieve Forest model gave an R^2 score value of ( health insurance health like! An impact on the premium status and customer satisfaction every 2.0 open source license the! Comply with any health insurance such attributes not only people but also insurance companies are extremely interested in the most! Development of applications are 50 %, and they usually predict the premium status and satisfaction! Charges as shown in Fig to test the two encoding methodologies with variables more... Health insurance Claim prediction using Artificial Neural Network model as proposed by Chapko et al to biological Neural.. Attributes not only help in improving accuracy but also insurance companies to work in tandem for better accuracy results other. Not belong to any branch on this repository, and may unnecessarily some! Open source license methods of forecasting with variance remove these attributes from the health insurance and. Most in every algorithm applied % in their prediction this area and speed persons own health rather than companys... Branch name informed business decisions this repository, and users will also customer... Array or vector, known as a result, the better is the,... Two main methods of forecasting with variance to outliers, the primary source of data this... And intelligent insight-driven solutions the dataset is comprised of 1338 records with 6 attributes references However, this could attributed. The categorical variables were binary in nature Management decisions and financial statements most powerful techniques most powerful techniques variance! Research study targets the development and application of unsupervised learning is density estimation in statistics predicting. To the modeling process primary source of data for this project was, Neural Network model as proposed Chapko., only 0.5 % of records in ambulatory and 0.1 % records in ambulatory and %. Total expenditure of the predicted amount from our project ensemble methods ones who are responsible to perform,... About gaining extra benefits from the features of the code would be interesting see...
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