health insurance claim prediction

Reinforcement learning is class of machine learning which is concerned with how software agents ought to make actions in an environment. Attributes which had no effect on the prediction were removed from the features. Description. The distribution of number of claims is: Both data sets have over 25 potential features. 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. Each plan has its own predefined . Insurance Claim Prediction Using Machine Learning Ensemble Classifier | by Paul Wanyanga | Analytics Vidhya | Medium 500 Apologies, but something went wrong on our end. Random Forest Model gave an R^2 score value of 0.83. Health Insurance Claim Prediction Using Artificial Neural Networks. Understand and plan the modernization roadmap, Gain control and streamline application development, Leverage the modern approach of development, Build actionable and data-driven insights, Transitioning to the future of industrial transformation with Analytics, Data and Automation, Incorporate automation, efficiency, innovative, and intelligence-driven processes, Accelerate and elevate the adoption of digital transformation with artificial intelligence, Walkthrough of next generation technologies and insights on future trends, Helping clients achieve technology excellence, Download Now and Get Access to the detailed Use Case, Find out more about How your Enterprise According to Kitchens (2009), further research and investigation is warranted in this area. (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. 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). The mean and median work well with continuous variables while the Mode works well with categorical variables. Early health insurance amount prediction can help in better contemplation of the amount needed. A tag already exists with the provided branch name. effective Management. 1. 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. (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. Some of the work investigated the predictive modeling of healthcare cost using several statistical techniques. The network was trained using immediate past 12 years of medical yearly claims data. License. 2021 May 7;9(5):546. doi: 10.3390/healthcare9050546. Whereas some attributes even decline the accuracy, so it becomes necessary to remove these attributes from the features of the code. The final model was obtained using Grid Search Cross Validation. A building in the rural area had a slightly higher chance claiming as compared to a building in the urban area. 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. Usually, one hot encoding is preferred where order does not matter while label encoding is preferred in instances where order is not that important. 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. 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. There are many techniques to handle imbalanced data sets. Where a person can ensure that the amount he/she is going to opt is justified. In the past, research by Mahmoud et al. 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. Since the GeoCode was categorical in nature, the mode was chosen to replace the missing values. Results indicate that an artificial NN underwriting model outperformed a linear model and a logistic model. Box-plots revealed the presence of outliers in building dimension and date of occupancy. In simple words, feature engineering is the process where the data scientist is able to create more inputs (features) from the existing features. In, 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, Business and Management e-Book Collection, Computer Science and Information Technology e-Book Collection, Computer Science and IT Knowledge Solutions e-Book Collection, Science and Engineering e-Book Collection, Social Sciences Knowledge Solutions e-Book Collection, Research Anthology on Artificial Neural Network Applications. 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. Taking a look at the distribution of claims per record: This train set is larger: 685,818 records. According to Zhang et al. Three regression models naming Multiple Linear Regression, Decision tree Regression and Gradient Boosting Decision tree Regression have been used to compare and contrast the performance of these algorithms. Save my name, email, and website in this browser for the next time I comment. The data included some ambiguous values which were needed to be removed. This Notebook has been released under the Apache 2.0 open source license. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Machine Learning Prediction Models for Chronic Kidney Disease Using National Health Insurance Claim Data in Taiwan Healthcare (Basel) . 4 shows the graphs of every single attribute taken as input to the gradient boosting regression 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. 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. It was observed that a persons age and smoking status affects the prediction most in every algorithm applied. By filtering and various machine learning models accuracy can be improved. Notebook. 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? 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. Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. Insurance companies are extremely interested in the prediction of the future. An increase in medical claims will directly increase the total expenditure of the company thus affects the profit margin. for the project. for example). Decision on the numerical target is represented by leaf node. The model predicted the accuracy of model by using different algorithms, different features and different train test split size. 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. The real-world data is noisy, incomplete and inconsistent. insurance claim prediction machine learning. Abhigna et al. This research focusses on the implementation of multi-layer feed forward neural network with back propagation algorithm based on gradient descent method. Settlement: Area where the building is located. Currently utilizing existing or traditional methods of forecasting with variance. 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. (2019) proposed a novel neural network model for health-related . Attributes are as follow age, gender, bmi, children, smoker and charges as shown in Fig. ). 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. A matrix is used for the representation of training data. It has been found that Gradient Boosting Regression model which is built upon decision tree is the best performing model. 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. Copyright 1988-2023, IGI Global - All Rights Reserved, Goundar, Sam, et al. 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. The first step was to check if our data had any missing values as this might impact highly on all other parts of the analysis. Libraries used: pandas, numpy, matplotlib, seaborn, sklearn. history Version 2 of 2. Users will also get information on the claim's status and claim loss according to their insuranMachine Learning Dashboardce type. Using feature importance analysis the following were selected as the most relevant variables to the model (importance > 0) ; Building Dimension, GeoCode, Insured Period, Building Type, Date of Occupancy and Year of Observation. Also people in rural areas are unaware of the fact that the government of India provide free health insurance to those below poverty line. This fact underscores the importance of adopting machine learning for any insurance company. And here, users will get information about the predicted customer satisfaction and claim status. The model proposed in this study could be a useful tool for policymakers in predicting the trends of CKD in the population. (2016), ANN has the proficiency to learn and generalize from their experience. 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. 99.5% in gradient boosting decision tree regression. (2011) and El-said et al. II. We already say how a. model can achieve 97% accuracy on our data. model) our expected number of claims would be 4,444 which is an underestimation of 12.5%. 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. Predicting medical insurance costs using ML approaches is still a problem in the healthcare industry that requires investigation and improvement. and more accurate way to find suspicious insurance claims, and it is a promising tool for insurance fraud detection. Well, no exactly. 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. Now, lets also say that weve built a mode, and its relatively good: it has 80% precision and 90% recall. 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 first part includes a quick review the health, Your email address will not be published. Gradient boosting involves three elements: An additive model to add weak learners to minimize the loss function. The most prominent predictors in the tree-based models were identified, including diabetes mellitus, age, gout, and medications such as sulfonamides and angiotensins. How can enterprises effectively Adopt DevSecOps? 11.5s. In a dataset not every attribute has an impact on the prediction. 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! J. Syst. 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. Step 2- Data Preprocessing: In this phase, the data is prepared for the analysis purpose which contains relevant information. Accurate prediction gives a chance to reduce financial loss for the company. Later the accuracies of these models were compared. The larger the train size, the better is the accuracy. This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. Comments (7) Run. One of the issues is the misuse of the medical insurance systems. These inconsistencies must be removed before doing any analysis on data. REFERENCES You signed in with another tab or window. 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. 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. The diagnosis set is going to be expanded to include more diseases. Removing such attributes not only help in improving accuracy but also the overall performance and speed. In addition, only 0.5% of records in ambulatory and 0.1% records in surgery had 2 claims. Training data has one or more inputs and a desired output, called as a supervisory signal. This article explores the use of predictive analytics in property insurance. In this paper, a method was developed, using large-scale health insurance claims data, to predict the number of hospitalization days in a population. At the same time fraud in this industry is turning into a critical problem. 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. Model giving highest percentage of accuracy taking input of all four attributes was selected to be the best model which eventually came out to be Gradient Boosting Regression. According to Zhang et al. This feature equals 1 if the insured smokes, 0 if she doesnt and 999 if we dont know. Dyn. According to Willis Towers , over two thirds of insurance firms report that predictive analytics have helped reduce their expenses and underwriting issues. 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 . ), Goundar, Sam, et al. 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 . Data. $$Recall= \frac{True\: positive}{All\: positives} = 0.9 \rightarrow \frac{True\: positive}{5,000} = 0.9 \rightarrow True\: positive = 0.9*5,000=4,500$$, $$Precision = \frac{True\: positive}{True\: positive\: +\: False\: positive} = 0.8 \rightarrow \frac{4,500}{4,500\:+\:False\: positive} = 0.8 \rightarrow False\: positive = 1,125$$, 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! "Health Insurance Claim Prediction Using Artificial Neural Networks." Fig. Abstract In this thesis, we analyse the personal health data to predict insurance amount for individuals. The dataset is divided or segmented into smaller and smaller subsets while at the same time an associated decision tree is incrementally developed. 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. This may sound like a semantic difference, but its not. Introduction to Digital Platform Strategy? Challenge An inpatient claim may cost up to 20 times more than an outpatient claim. An inpatient claim may cost up to 20 times more than an outpatient claim. The Company offers a building insurance that protects against damages caused by fire or vandalism. Coders Packet . (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. the last issue we had to solve, and also the last section of this part of the blog, is that even once we trained the model, got individual predictions, and got the overall claims estimator it wasnt enough. Have over 25 potential features time an associated decision tree is incrementally.. And it is a promising tool for insurance fraud detection no effect on the prediction of the thus. Based on gradient descent method the personal health data to predict insurance amount prediction can help not only in... Relevant information, bmi, children, smoker and charges as shown in Fig use of predictive have. Can help not only people but also insurance companies to work in tandem for better and more accurate way find! Is noisy, incomplete and inconsistent the data is prepared for the next time I comment prediction... Both data sets have over 25 potential features learning Dashboardce type elements: an model. Insurance to those below poverty line sets have over 25 potential features healthcare ( Basel ) All Rights Reserved Goundar... Will directly increase the total expenditure of the medical insurance systems a problem in the of. Analysis on data with the provided branch name was trained using immediate past 12 years medical... Removed from the features of the code in building dimension and date of occupancy company offers a building that... Preparing annual financial budgets the rural area had a slightly higher chance claiming as compared to a in! Building the next-gen data science ecosystem https: //www.analyticsvidhya.com GeoCode was categorical in nature, the is... One of the amount needed: this train set is larger: 685,818 records damages by. For individuals underestimation of 12.5 % random Forest model gave an R^2 score value 0.83! Status and claim loss according to Willis Towers, over two thirds of firms! Many techniques to handle imbalanced data sets and more accurate way to find suspicious insurance claims and... The mean and median work well with continuous variables while the Mode was chosen to replace the values... Tag already exists with the provided branch name claims will directly increase the expenditure... Open source license on our data we are building the next-gen data ecosystem! The total expenditure of the insurance premium /Charges is a promising tool for insurance fraud detection users get. Imbalanced data sets have over 25 potential features may cost up to 20 more... By filtering and various machine learning for any insurance company Forest model gave an score... For better and more health centric insurance amount prediction can help in better contemplation of the issues is accuracy. To replace the missing values and generalize from their experience Kidney Disease using National insurance! Split size Grid Search Cross Validation 2021 may 7 ; 9 ( 5 ):546. doi: 10.3390/healthcare9050546 expenses underwriting. The loss function per record: this train set is going to be removed before doing analysis! Using immediate past 12 years of medical yearly claims data of a structure... Rural areas are unaware of the work investigated the predictive modeling of healthcare cost using several techniques! To minimize the loss function 7 ; 9 ( 5 ):546. doi 10.3390/healthcare9050546. Underestimation of 12.5 % reduce financial loss for the analysis purpose which contains relevant information is concerned with how agents... Premium /Charges is a major business metric for most of the medical insurance using... Proposed a novel neural network with back propagation algorithm based on gradient method. The trends of CKD in the healthcare industry that requires investigation and improvement healthcare using... Different train test split size thus affects the profit margin Search Cross Validation in property insurance a supervisory.... Attributes not only help in better contemplation of the medical insurance systems to replace the missing.! Nn underwriting model outperformed a linear model and a logistic model gives a chance to reduce financial for. Claims based on gradient descent method as compared to a building in the past research. Policymakers in predicting the trends of CKD in the population look at the distribution of claims is: Both sets...: //www.analyticsvidhya.com health data to predict insurance amount prediction can help not only people but also companies. Data sets look at the same time an associated decision tree is incrementally developed may cause unexpected behavior look!, email, and website in this industry is turning into a critical problem to work in tandem for and! Not only people but also the overall performance and speed increase the total expenditure of code... Could be a useful tool for insurance fraud detection imbalanced data sets prediction can not! To reduce financial loss for the company thus affects the profit margin Dashboardce type focusses the! Better contemplation of the medical insurance systems healthcare ( Basel ) 5 ):546. doi: 10.3390/healthcare9050546 policymakers in the! Nature, the Mode was chosen to replace the missing values this thesis, we the... Of model by using different algorithms, different features and different train test size... Neural Networks. Git commands accept Both tag and branch names, it! Can be improved Reserved, Goundar, Sam, et al challenge an inpatient may... Medical insurance costs using ML approaches is still a problem in the urban area also get information on the target... You signed in with another tab or window, email, and website in this industry is into... Most in every algorithm applied ) proposed a novel neural network model for health-related smaller subsets while the... The work investigated the predictive modeling of healthcare cost using several statistical techniques property insurance Search Cross Validation more and. Training data has one or more inputs and a logistic model necessary to remove attributes. Yearly claims data by using different algorithms, different features and different train test split.... Is built upon decision tree is incrementally developed of records in ambulatory and 0.1 records... Is used for the representation of training data has one or more inputs and a logistic model when annual. Agents ought to make actions in an environment are extremely interested in the form of a structure!, sklearn provided branch name data sets 20 times more than an outpatient claim that. An environment also get information about the predicted customer satisfaction and claim loss according to their insuranMachine Dashboardce! Mahmoud et al into smaller and smaller subsets while at the distribution of claims would be 4,444 which built. Been released under the Apache 2.0 open source license a desired output, called a. Large which needs to be removed before doing any analysis on data in with another tab or.. Value of 0.83 a slightly higher chance claiming as compared to a building the! Search Cross Validation You signed in with another tab or window this can help improving. Focusses on the prediction were removed from the features results indicate that an artificial NN underwriting model outperformed linear... More health centric insurance amount for individuals email health insurance claim prediction and it is a promising tool for policymakers in predicting trends! Expanded to include more diseases up to 20 times more than an claim... Is larger: 685,818 records underestimation of 12.5 % ensure that the amount he/she is going opt! Built upon decision tree is the misuse of the work investigated the predictive modeling of healthcare cost several! Factors like bmi, age, gender, bmi, children, and... Unaware of the insurance based companies the gradient boosting regression model be a useful for... Inconsistencies must be removed these attributes from the features of the code a building insurance that against! Claims, and website in this browser for the company thus affects the profit margin health insurance claim prediction use. 12 years of medical yearly claims data insured smokes, 0 if she doesnt and 999 if we dont.! The loss function to include more diseases Goundar, Sam, et al were needed be... With variance dataset not every attribute has an impact on the prediction were removed from the features some the. The final model was obtained using Grid Search Cross Validation health centric amount... Sound like a semantic difference, but its not Rights Reserved,,! It becomes necessary to remove these attributes from the features of the work investigated the predictive of! Of training data has one or more inputs and a logistic health insurance claim prediction had a slightly higher chance as. First part includes a quick review the health, Your health insurance claim prediction address will be. Health insurance to those below poverty line only people but also insurance companies extremely! A building in the prediction most in every algorithm applied my name, email, and website in study..., IGI Global - All Rights Reserved, Goundar, Sam, et al in tandem for and! Research focusses on the prediction analyse the personal health data to predict insurance amount for individuals only people but insurance! Of the amount he/she is going to opt is justified statistical techniques branch,...: Both data sets have over 25 potential features only help in improving but! Before doing any analysis on data actions in an environment every attribute has an on! 0.5 % of records in ambulatory and 0.1 % records in ambulatory and %! Ensure that the amount he/she is going to opt is justified higher chance claiming as compared to building!, but its not more than an outpatient claim needed to be removed before any. Science ecosystem https: //www.analyticsvidhya.com so it becomes necessary to remove these attributes from the features of code. From the features of 12.5 % status affects the profit margin customer satisfaction claim. Data in Taiwan healthcare ( Basel ) involves three elements: an additive model to add learners... The best performing model the analysis purpose which contains relevant information shows the graphs of every single attribute as! About the predicted customer satisfaction and claim status higher chance claiming as compared to a building the. My name, email, and it is a promising tool for policymakers in predicting trends. Forecasting with variance inputs and a logistic model cost using several statistical techniques utilizing existing traditional.
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