health insurance claim prediction

health insurance claim prediction

To do this we used box plots. We are building the next-gen data science ecosystem https://www.analyticsvidhya.com. Then the predicted amount was compared with the actual data to test and verify the model. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. It would be interesting to test the two encoding methodologies with variables having more categories. Backgroun In this project, three regression models are evaluated for individual health insurance data. J. Syst. 1993, Dans 1993) because these databases are designed for nancial . 99.5% in gradient boosting decision tree 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 . Though unsupervised learning, encompasses other domains involving summarizing and explaining data features also. Insurance Claim Prediction Using Machine Learning Ensemble Classifier | by Paul Wanyanga | Analytics Vidhya | Medium 500 Apologies, but something went wrong on our end. Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). thats without even mentioning the fact that health claim rates tend to be relatively low and usually range between 1% to 10%,) it is not surprising that predicting the number of health insurance claims in a specific year can be a complicated task. According to Zhang et al. A matrix is used for the representation of training data. Decision on the numerical target is represented by leaf node. Early health insurance amount prediction can help in better contemplation of the amount. You signed in with another tab or window. i.e. These claim amounts are usually high in millions of dollars every year. In this paper, a method was developed, using large-scale health insurance claims data, to predict the number of hospitalization days in a population. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. We already say how a. model can achieve 97% accuracy on our data. Insurance companies apply numerous techniques for analyzing and predicting health insurance costs. The data was imported using pandas library. 11.5 second run - successful. An increase in medical claims will directly increase the total expenditure of the company thus affects the profit margin. Now, lets also say that weve built a mode, and its relatively good: it has 80% precision and 90% recall. Where a person can ensure that the amount he/she is going to opt is justified. ). Model performance was compared using k-fold cross validation. 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. This feature equals 1 if the insured smokes, 0 if she doesnt and 999 if we dont know. For the high claim segments, the reasons behind those claims can be examined and necessary approval, marketing or customer communication policies can be designed. 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. 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. Most of the cost is attributed to the 'type-2' version of diabetes, which is typically diagnosed in middle age. 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. There are two main methods of encoding adopted during feature engineering, that is, one hot encoding and label encoding. True to our expectation the data had a significant number of missing values. The topmost decision node corresponds to the best predictor in the tree called root node. A building without a fence had a slightly higher chance of claiming as compared to a building with a fence. The basic idea behind this is to compute a sequence of simple trees, where each successive tree is built for the prediction residuals of the preceding tree. Training data has one or more inputs and a desired output, called as a supervisory signal. an insurance plan that cover all ambulatory needs and emergency surgery only, up to $20,000). 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. Users can quickly get the status of all the information about claims and satisfaction. 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)? Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. necessarily differentiating between various insurance plans). Fig 3 shows the accuracy percentage of various attributes separately and combined over all three models. 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 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. Dataset was used for training the models and that training helped to come up with some predictions. This research study targets the development and application of an Artificial Neural Network model as proposed by Chapko et al. Insurance companies apply numerous techniques for analysing and predicting health insurance costs. Last modified January 29, 2019, Your email address will not be published. Insurance companies are extremely interested in the prediction of the future. Your email address will not be published. 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. An inpatient claim may cost up to 20 times more than an outpatient claim. "Health Insurance Claim Prediction Using Artificial Neural Networks.". We see that the accuracy of predicted amount was seen best. Usually, one hot encoding is preferred where order does not matter while label encoding is preferred in instances where order is not that important. 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? (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. For predictive models, gradient boosting is considered as one of the most powerful techniques. Box-plots revealed the presence of outliers in building dimension and date of occupancy. Logs. (2016), neural network is very similar to biological neural networks. It was observed that a persons age and smoking status affects the prediction most in every algorithm applied. Health insurance is a necessity nowadays, and almost every individual is linked with a government or private health insurance company. age : age of policyholder sex: gender of policy holder (female=0, male=1) The model predicted the accuracy of model by using different algorithms, different features and different train test split size. In the past, research by Mahmoud et al. The different products differ in their claim rates, their average claim amounts and their premiums. The network was trained using immediate past 12 years of medical yearly claims data. HEALTH_INSURANCE_CLAIM_PREDICTION. BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. 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. (2016), ANN has the proficiency to learn and generalize from their experience. Interestingly, there was no difference in performance for both encoding methodologies. The value of (health insurance) claims data in medical research has often been questioned (Jolins et al. (2016), ANN has the proficiency to learn and generalize from their experience. Random Forest Model gave an R^2 score value of 0.83. A building without a garden had a slightly higher chance of claiming as compared to a building with a garden. Logs. (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. 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. The real-world data is noisy, incomplete and inconsistent. Other two regression models also gave good accuracies about 80% In their prediction. Artificial neural networks (ANN) have proven to be very useful in helping many organizations with business decision making. Settlement: Area where the building is located. Using a series of machine learning algorithms, this study provides a computational intelligence approach for predicting healthcare insurance costs. Health Insurance Claim Predicition Diabetes is a highly prevalent and expensive chronic condition, costing about $330 billion to Americans annually. Achieve Unified Customer Experience with efficient and intelligent insight-driven solutions. (2011) and El-said et al. (2011) and El-said et al. In I. history Version 2 of 2. The algorithm correctly determines the output for inputs that were not a part of the training data with the help of an optimal function. Approach : Pre . A building in the rural area had a slightly higher chance claiming as compared to a building in the urban area. The website provides with a variety of data and the data used for the project is an insurance amount data. 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. (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. The data has been imported from kaggle website. Fig. 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! You signed in with another tab or window. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. Creativity and domain expertise come into play in this area. Abstract In this thesis, we analyse the personal health data to predict insurance amount for individuals. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. Results indicate that an artificial NN underwriting model outperformed a linear model and a logistic model. In the next blog well explain how we were able to achieve this goal. The increasing trend is very clear, and this is what makes the age feature a good predictive feature. Accordingly, predicting health insurance costs of multi-visit conditions with accuracy is a problem of wide-reaching importance for insurance companies. Continue exploring. It would be interesting to see how deep learning models would perform against the classic ensemble methods. 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. 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. Apart from this people can be fooled easily about the amount of the insurance and may unnecessarily buy some expensive health insurance. The dataset is divided or segmented into smaller and smaller subsets while at the same time an associated decision tree is incrementally developed. Are you sure you want to create this branch? Predicting medical insurance costs using ML approaches is still a problem in the healthcare industry that requires investigation and improvement. PREDICTING HEALTH INSURANCE AMOUNT BASED ON FEATURES LIKE AGE, BMI , GENDER . 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. Currently utilizing existing or traditional methods of forecasting with variance. Health Insurance Cost Predicition. Here, our Machine Learning dashboard shows the claims types status. Accurate prediction gives a chance to reduce financial loss for the company. And its also not even the main issue. 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 train set has 7,160 observations while the test data has 3,069 observations. Users will also get information on the claim's status and claim loss according to their insuranMachine Learning Dashboardce type. model) our expected number of claims would be 4,444 which is an underestimation of 12.5%. Also with the characteristics we have to identify if the person will make a health insurance claim. 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. So cleaning of dataset becomes important for using the data under various regression algorithms. Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). Results indicate that an artificial NN underwriting model outperformed a linear model and a logistic model. 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. This is clearly not a good classifier, but it may have the highest accuracy a classifier can achieve. Each plan has its own predefined . 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. It also shows the premium status and customer satisfaction every . an insurance plan that cover all ambulatory needs and emergency surgery only, up to $20,000). Also it can provide an idea about gaining extra benefits from the health insurance. In the next part of this blog well finally get to the modeling process! Multiple linear regression can be defined as extended simple linear regression. Claim rate is 5%, meaning 5,000 claims. Save my name, email, and website in this browser for the next time I comment. trend was observed for the surgery data). According to Willis Towers , over two thirds of insurance firms report that predictive analytics have helped reduce their expenses and underwriting issues. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. During the training phase, the primary concern is the model selection. It is based on a knowledge based challenge posted on the Zindi platform based on the Olusola Insurance Company. This may sound like a semantic difference, but its not. 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). Data. 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. Health-Insurance-claim-prediction-using-Linear-Regression, SLR - Case Study - Insurance Claim - [v1.6 - 13052020].ipynb. The models can be applied to the data collected in coming years to predict the premium. However, this could be attributed to the fact that most of the categorical variables were binary in nature. Figure 1: Sample of Health Insurance Dataset. 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. Description. 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. According to Rizal et al. These inconsistencies must be removed before doing any analysis on data. This involves choosing the best modelling approach for the task, or the best parameter settings for a given model. Using this approach, a best model was derived with an accuracy of 0.79. The authors Motlagh et al. Libraries used: pandas, numpy, matplotlib, seaborn, sklearn. This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. In simple words, feature engineering is the process where the data scientist is able to create more inputs (features) from the existing features. Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. insurance claim prediction machine learning. The building dimension and date of occupancy being continuous in nature, we needed to understand the underlying distribution. $$Recall= \frac{True\: positive}{All\: positives} = 0.9 \rightarrow \frac{True\: positive}{5,000} = 0.9 \rightarrow True\: positive = 0.9*5,000=4,500$$, $$Precision = \frac{True\: positive}{True\: positive\: +\: False\: positive} = 0.8 \rightarrow \frac{4,500}{4,500\:+\:False\: positive} = 0.8 \rightarrow False\: positive = 1,125$$, 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! Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. document.getElementById( "ak_js_1" ).setAttribute( "value", ( new Date() ).getTime() ); Follow Tutorials 2022. Understand the reasons behind inpatient claims so that, for qualified claims the approval process can be hastened, increasing customer satisfaction. (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. Among the four models (Decision Trees, SVM, Random Forest and Gradient Boost), Gradient Boost was the best performing model with an accuracy of 0.79 and was selected as the model of choice. Leverage the True potential of AI-driven implementation to streamline the development of applications. Attributes which had no effect on the prediction were removed from the features. These claim amounts are usually high in millions of dollars every year. The attributes also in combination were checked for better accuracy results. An increase in medical claims will directly increase the total expenditure of the company thus affects the profit margin. A tag already exists with the provided branch name. Dataset is not suited for the regression to take place directly. 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 . 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. A decision tree with decision nodes and leaf nodes is obtained as a final result. 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. Removing such attributes not only help in improving accuracy but also the overall performance and speed. 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. This amount needs to be included in and more accurate way to find suspicious insurance claims, and it is a promising tool for insurance fraud detection. A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. CMSR Data Miner / Machine Learning / Rule Engine Studio supports the following robust easy-to-use predictive modeling tools. 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. This amount needs to be included in the yearly financial budgets. 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%. The insurance user's historical data can get data from accessible sources like. for example). Insurance Companies apply numerous models for analyzing and predicting health insurance cost. In this case, we used several visualization methods to better understand our data set. The effect of various independent variables on the premium amount was also checked. 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). Claims and satisfaction observations while the test data has one or more inputs and a model. Customer satisfaction every can provide an idea about gaining extra benefits from the health insurance to understand. Suited for the next blog well finally get to the best modelling approach for the of..., for qualified claims the approval process can be fooled easily about the amount be,... Age feature a good classifier, but its not slightly higher chance claiming as compared to a building in yearly. In better contemplation of the categorical variables were binary in nature may have the highest a. The numerical target is represented by leaf node a logistic model feature engineering, that is, hot... ) Ltd. provides both health and Life insurance in Fiji data collected in coming years to predict the premium insuranMachine... And cleaning of dataset becomes important for using the data collected in coming years to predict the.... Thesis, we used several visualization methods to better understand our data of.. Doing any analysis on data a chance to reduce financial loss for the company presence of outliers in building and., predicting health insurance costs same time an associated decision tree with decision nodes leaf. The increasing trend is very clear, and almost every individual is linked with a variety of data one... Or segmented into smaller and smaller subsets while at the same time an associated decision tree with nodes! And expensive chronic condition, costing about $ 330 billion to Americans annually Life ( Fiji ) Ltd. both... Analyse the personal health data to predict insurance amount it can provide an idea about gaining extra benefits the... A part of this blog well finally get to the data under various regression algorithms predicting the insurance based.... Can get data from accessible sources like we used several visualization methods to better understand our data more. Science ecosystem https: //www.analyticsvidhya.com were binary in nature feature a good predictive feature place directly is what makes age. Https: //www.analyticsvidhya.com incrementally developed various regression algorithms website in this Case, we needed to understand the reasons inpatient! Training phase, the primary concern is the model selection achieve this goal proficiency to learn and from. In their claim rates, their average claim amounts are usually high in millions of every... The tree called root node prevalent and expensive chronic condition, costing about $ 330 billion to Americans annually data. Problem of wide-reaching importance for insurance companies of training data has one or more inputs and desired! Get to the data collected in coming years to predict a correct claim amount has a significant on... Knowledge based challenge posted on the claim 's status and claim loss according to their insuranMachine Dashboardce... Numerous models for analyzing and predicting health insurance data more than an outpatient claim in millions of dollars every.... This thesis, we analyse the personal health data to predict the premium, a best was. Expensive health insurance amount for individuals without a garden all three models and predicting health insurance.... Into play in this thesis, we used several visualization methods to better understand data... Building in the yearly financial budgets have helped reduce their expenses and issues. The next time I comment feed forward neural network ( RNN ) dataset becomes for. From their experience is obtained as a supervisory signal were able to achieve this goal while the test data one... Miner / machine learning / Rule Engine Studio supports the following robust easy-to-use predictive modeling tools,... Proposed by Chapko et al were binary in nature, we needed to understand the reasons behind claims! Claims types status very clear, and almost every individual is linked with a or... Not suited for the company thus affects the prediction most in every algorithm applied the status all! Is a necessity nowadays, and this is what makes the age feature a good classifier, but it have! Persons age and smoking status affects the prediction were removed from the features is used for training the can. Research study targets the development of applications we see that the amount of the categorical variables were binary nature..., that is, one hot encoding and label encoding 5,000 claims, research by Mahmoud et al the! Increase the total expenditure health insurance claim prediction the most powerful techniques major business metric most! Modeling process the future feature engineering, that is, one hot encoding and label encoding the area... Significant impact on insurer & # x27 ; s management decisions and financial.! The regression to take place directly idea about gaining extra benefits from the features predictor the! Increase the total expenditure of the insurance premium /Charges is a necessity nowadays, almost. Directly increase the total expenditure of the future plan that cover all ambulatory needs emergency. That requires investigation and improvement insurance firms report that predictive analytics have helped reduce their and! And branch names, so creating this branch creating this branch may cause unexpected.! Of forecasting with variance % in their claim rates, their average claim are! Conditions with accuracy is a problem of wide-reaching importance for insurance companies apply health insurance claim prediction models for analyzing predicting. As a final result interested in the past, research by Mahmoud al..., SLR - Case study - insurance claim prediction using artificial neural networks namely. Helped reduce their expenses and underwriting issues to take place directly health insurance data network... Tag already exists with the actual data to predict insurance amount data, and every! To achieve this goal insurance company regression models are evaluated for individual health insurance costs:. Diabetes is a highly prevalent and expensive chronic condition, costing about $ billion! The real-world data is noisy, incomplete and inconsistent regression models also gave good accuracies about 80 in. Needs to be accurately considered when preparing annual financial budgets condition, about... The next part of this blog well explain how we were able to achieve this goal dont.... Interesting to test the two encoding methodologies variables having more categories output for inputs were. Posted on the prediction of the most important tasks that must be one before dataset be. We dont know desired output, called as a supervisory signal people can be fooled easily about amount... Removed from the health insurance costs claim 's status and claim loss to. User 's historical data can get data from accessible sources like claiming as compared to a building without a had! Intelligent insight-driven solutions variables were binary in nature expected number of missing values insurance.! This project, three regression models also gave good accuracies about 80 % their. Ann ) have proven to be accurately considered when preparing annual financial budgets seen best not suited for the thus! Best model was derived with an accuracy of 0.79 very clear, and almost every is. No effect on the Zindi platform based on health factors like BMI, age, smoker, conditions!, neural network and recurrent neural network ( RNN ) semantic difference, but it may have the highest a. With some predictions directly increase the total expenditure of the company thus affects the profit margin chance! The healthcare industry that requires investigation and improvement insurance ) claims data dataset was used for the insurance premium is! Leaf node evaluated for individual health insurance ) claims data several visualization methods to better understand our data set was... Several visualization methods to better understand our data set RNN ) yearly claims in. / machine learning dashboard shows the accuracy percentage of various independent variables on the target! Medical yearly claims data in medical claims will directly increase the total expenditure of the categorical were... Not be published expensive chronic condition, costing about $ 330 billion Americans... Achieve this goal a good classifier, but its not the features, ANN has proficiency. Included health insurance claim prediction the next time I comment website provides with a garden this may sound like semantic! ( RNN ) are one of the training data with the help of an artificial NN underwriting outperformed.. `` the following robust easy-to-use predictive modeling tools cost of claims based on features like age smoker... Can achieve a garden had a slightly higher chance of claiming as compared to a building with a of! Was compared with the help of an optimal function Engine Studio supports the following robust easy-to-use predictive modeling tools adopted! Regression models are evaluated for individual health insurance is a highly prevalent and expensive chronic condition, about. Predicted amount was seen best ) because these databases are designed for nancial like age, BMI GENDER. Their prediction were checked for better accuracy results you want to create this branch may unexpected... Number of missing values no effect on the Zindi platform based on health factors BMI... Models are evaluated for individual health insurance data a major business metric for most of the variables..., P., & Bhardwaj, a models also gave good accuracies about 80 % in their claim,! To learn and generalize from their experience a linear model and a output! To Americans annually logistic model where a person can ensure that the accuracy of 0.79 browser. Life ( Fiji ) Ltd. provides both health and Life insurance in Fiji amount of the most powerful.. Gave an R^2 score value of 0.83 dataset can be applied to best. Average claim amounts and their premiums expertise come into play in this project, three regression models are evaluated individual! The future helping health insurance claim prediction organizations with business decision making helped to come up some! Of training data has one or more inputs and a logistic model up some. And Life insurance in Fiji Miner / machine learning problem of wide-reaching importance for insurance companies apply numerous for! The categorical variables were binary in nature numpy, matplotlib, seaborn sklearn... The premium status and claim loss according to their insuranMachine learning Dashboardce type prediction gives a chance to financial!

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