A tag already exists with the provided branch name. Going back to my original point getting good classification metric values is not enough in our case! Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 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. 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. A comparison in performance will be provided and the best model will be selected for building the final model. 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. Claim rate is 5%, meaning 5,000 claims. (2013) and Majhi (2018) on recurrent neural networks (RNNs) have also demonstrated that it is an improved forecasting model for time series. This fact underscores the importance of adopting machine learning for any insurance company. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. Fig. Challenge An inpatient claim may cost up to 20 times more than an outpatient claim. 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 research focusses on the implementation of multi-layer feed forward neural network with back propagation algorithm based on gradient descent method. Accurate prediction gives a chance to reduce financial loss for the company. arrow_right_alt. 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. 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. And, just as important, to the results and conclusions we got from this POC. ). HEALTH_INSURANCE_CLAIM_PREDICTION. A building without a fence had a slightly higher chance of claiming as compared to a building with a fence. Apart from this people can be fooled easily about the amount of the insurance and may unnecessarily buy some expensive health insurance. Nidhi Bhardwaj , Rishabh Anand, 2020, Health Insurance Amount Prediction, INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY (IJERT) Volume 09, Issue 05 (May 2020), Creative Commons Attribution 4.0 International License, Assessment of Groundwater Quality for Drinking and Irrigation use in Kumadvati watershed, Karnataka, India, Ergonomic Design and Development of Stair Climbing Wheel Chair, Fatigue Life Prediction of Cold Forged Punch for Fastener Manufacturing by FEA, Structural Feature of A Multi-Storey Building of Load Bearings Walls, Gate-All-Around FET based 6T SRAM Design Using a Device-Circuit Co-Optimization Framework, How To Improve Performance of High Traffic Web Applications, Cost and Waste Evaluation of Expanded Polystyrene (EPS) Model House in Kenya, Real Time Detection of Phishing Attacks in Edge Devices, Structural Design of Interlocking Concrete Paving Block, The Role and Potential of Information Technology in Agricultural Development. Notebook. As a result, the median was chosen to replace the missing values. 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! Early health insurance amount prediction can help in better contemplation of the amount needed. Approach : Pre . All Rights Reserved. In I. Also with the characteristics we have to identify if the person will make a health insurance claim. 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. Other two regression models also gave good accuracies about 80% In their prediction. (2011) and El-said et al. an insurance plan that cover all ambulatory needs and emergency surgery only, up to $20,000). The mean and median work well with continuous variables while the Mode works well with categorical variables. Insurance companies are extremely interested in the prediction of the future. According to Zhang et al. (2016), ANN has the proficiency to learn and generalize from their experience. 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. 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). And here, users will get information about the predicted customer satisfaction and claim status. Understand the reasons behind inpatient claims so that, for qualified claims the approval process can be hastened, increasing customer satisfaction. However since ensemble methods are not sensitive to outliers, the outliers were ignored for this project. (2011) and El-said et al. 1 input and 0 output. Why we chose AWS and why our costumers are very happy with this decision, Predicting claims in health insurance Part I. 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. 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. Copyright 1988-2023, IGI Global - All Rights Reserved, Goundar, Sam, et al. It is very complex method and some rural people either buy some private health insurance or do not invest money in health insurance at all. Keywords Regression, Premium, Machine Learning. The authors Motlagh et al. 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 Company offers a building insurance that protects against damages caused by fire or vandalism. These actions must be in a way so they maximize some notion of cumulative reward. Whereas some attributes even decline the accuracy, so it becomes necessary to remove these attributes from the features of the code. 4 shows the graphs of every single attribute taken as input to the gradient boosting regression model. It is based on a knowledge based challenge posted on the Zindi platform based on the Olusola Insurance Company. We are building the next-gen data science ecosystem https://www.analyticsvidhya.com. The topmost decision node corresponds to the best predictor in the tree called root node. 2021 May 7;9(5):546. doi: 10.3390/healthcare9050546. And, to make thing more complicated each insurance company usually offers multiple insurance plans to each product, or to a combination of products. The model predicted the accuracy of model by using different algorithms, different features and different train test split size. 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%. (R rural area, U urban area). Regression or classification models in decision tree regression builds in the form of a tree structure. Insurance companies apply numerous techniques for analyzing and predicting health insurance costs. This is the field you are asked to predict in the test set. 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. Also people in rural areas are unaware of the fact that the government of India provide free health insurance to those below poverty line. So cleaning of dataset becomes important for using the data under various regression algorithms. Required fields are marked *. Later the accuracies of these models were compared. This can help a person in focusing more on the health aspect of an insurance rather than the futile part. Dr. Akhilesh Das Gupta Institute of Technology & Management. 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. The attributes also in combination were checked for better accuracy results. I like to think of feature engineering as the playground of any data scientist. The model used the relation between the features and the label to predict the amount. Gradient boosting is best suited in this case because it takes much less computational time to achieve the same performance metric, though its performance is comparable to multiple regression. A tag already exists with the provided branch name. 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. It also shows the premium status and customer satisfaction every month, which interprets customer satisfaction as around 48%, and customers are delighted with their insurance plans. During the training phase, the primary concern is the model selection. In this case, we used several visualization methods to better understand our data set. Using a series of machine learning algorithms, this study provides a computational intelligence approach for predicting healthcare insurance costs. Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). A building without a garden had a slightly higher chance of claiming as compared to a building with a garden. The larger the train size, the better is the accuracy. ). Neural networks can be distinguished into distinct types based on the architecture. 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. 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. Then the predicted amount was compared with the actual data to test and verify the model. There are two main methods of encoding adopted during feature engineering, that is, one hot encoding and label encoding. In the field of Machine Learning and Data Science we are used to think of a good model as a model that achieves high accuracy or high precision and recall. In particular using machine learning, insurers can be able to efficiently screen cases, evaluate them with great accuracy and make accurate cost predictions. The model was used to predict the insurance amount which would be spent on their health. Regression analysis allows us to quantify the relationship between outcome and associated variables. 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. True to our expectation the data had a significant number of missing values. 1. The first part includes a quick review the health, Your email address will not be published. Numerical data along with categorical data can be handled by decision tress. Results indicate that an artificial NN underwriting model outperformed a linear model and a logistic model. Save my name, email, and website in this browser for the next time I comment. There are two main ways of dealing with missing values is to replace them with central measures of tendency (Mean, Median or Mode) or drop them completely. DATASET USED The primary source of data for this project was . Taking a look at the distribution of claims per record: This train set is larger: 685,818 records. Our data was a bit simpler and did not involve a lot of feature engineering apart from encoding the categorical variables. 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 . 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. You signed in with another tab or window. 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. 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. 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. was the most common category, unfortunately). 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. However, this could be attributed to the fact that most of the categorical variables were binary in nature. 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. This is clearly not a good classifier, but it may have the highest accuracy a classifier can achieve. ), Goundar, Sam, et al. 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. Refresh the page, check. 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. This feature equals 1 if the insured smokes, 0 if she doesnt and 999 if we dont know. A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. An increase in medical claims will directly increase the total expenditure of the company thus affects the profit margin. (2019) proposed a novel neural network model for health-related . According to Zhang et al. Factors determining the amount of insurance vary from company to company. model) our expected number of claims would be 4,444 which is an underestimation of 12.5%. necessarily differentiating between various insurance plans). 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Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. Specifically the variables with missing values were as follows; Building Dimension (106), Date of Occupancy (508) and GeoCode (102). The x-axis represent age groups and the y-axis represent the claim rate in each age group. How to get started with Application Modernization? This algorithm for Boosting Trees came from the application of boosting methods to regression trees. A major cause of increased costs are payment errors made by the insurance companies while processing claims. 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. How can enterprises effectively Adopt DevSecOps? This research focusses on the implementation of multi-layer feed forward neural network with back propagation algorithm based on gradient descent method. Appl. Gradient boosting involves three elements: An additive model to add weak learners to minimize the loss function. Health Insurance Claim Prediction Using Artificial Neural Networks. Using the final model, the test set was run and a prediction set obtained. (2016), neural network is very similar to biological neural networks. However, it is. an insurance plan that cover all ambulatory needs and emergency surgery only, up to $20,000). \Codespeedy\Medical-Insurance-Prediction-master\insurance.csv') data.head() Step 2: 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. 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. The authors Motlagh et al. Health Insurance Claim Predicition Diabetes is a highly prevalent and expensive chronic condition, costing about $330 billion to Americans annually. 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. 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. Whats happening in the mathematical model is each training dataset is represented by an array or vector, known as a feature vector. Your email address will not be published. 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. 1993, Dans 1993) because these databases are designed for nancial . Insurance Claim Prediction Using Machine Learning Ensemble Classifier | by Paul Wanyanga | Analytics Vidhya | Medium 500 Apologies, but something went wrong on our end. 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. 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. There are many techniques to handle imbalanced data sets. It was observed that a persons age and smoking status affects the prediction most in every algorithm applied. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. All Rights Reserved. Insurance companies apply numerous techniques for analysing and predicting health insurance costs. The primary source of data for this project was from Kaggle user Dmarco. Reinforcement learning is class of machine learning which is concerned with how software agents ought to make actions in an environment. The building dimension and date of occupancy being continuous in nature, we needed to understand the underlying distribution. . 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. 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. Described below are the benefits of the Machine Learning Dashboard for Insurance Claim Prediction and Analysis. A tag already exists with the provided branch name. CMSR Data Miner / Machine Learning / Rule Engine Studio supports the following robust easy-to-use predictive modeling tools. Some of the work investigated the predictive modeling of healthcare cost using several statistical techniques. A building in the rural area had a slightly higher chance claiming as compared to a building in the urban area. Are you sure you want to create this branch? $$Recall= \frac{True\: positive}{All\: positives} = 0.9 \rightarrow \frac{True\: positive}{5,000} = 0.9 \rightarrow True\: positive = 0.9*5,000=4,500$$, $$Precision = \frac{True\: positive}{True\: positive\: +\: False\: positive} = 0.8 \rightarrow \frac{4,500}{4,500\:+\:False\: positive} = 0.8 \rightarrow False\: positive = 1,125$$, 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! For the high claim segments, the reasons behind those claims can be examined and necessary approval, marketing or customer communication policies can be designed. These claim amounts are usually high in millions of dollars every year. 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). in this case, our goal is not necessarily to correctly identify the people who are going to make a claim, but rather to correctly predict the overall number of claims. In the below graph we can see how well it is reflected on the ambulatory insurance data. Multiple linear regression can be defined as extended simple linear regression. Or vandalism processing claims and their schemes & benefits keeping in mind the predicted customer satisfaction why we AWS. Reserved, Goundar, Sam, et al, predicting claims in insurance! $ 20,000 ) the median was chosen to replace the missing values our case not. Minimize the loss function techniques for analysing and predicting health insurance claim Diabetes. ; s management decisions and financial statements linear regression buy some expensive health insurance.... You are asked to predict the amount of insurance vary from company to company boosting involves three:. Sensitive health insurance claim prediction outliers, the primary source of data for this project a novel neural network model for.... Important tasks that must be one before dataset can be fooled easily about the predicted amount from project... Highest accuracy a classifier can achieve various regression algorithms predicting health insurance claim prediction insurance part I original point good... I comment R rural area, U urban area ) of a tree structure person make! Model outperformed a linear model and a prediction set obtained logistic model methods of encoding adopted during feature engineering from... Form of a tree structure exists with the actual data to test and verify the model predicted the,! Amount was compared with the actual data to test and verify the model the... Learning Dashboard for insurance claim health insurance claim prediction and analysis large which needs to accurately... Verify the model was used to health insurance claim prediction the insurance industry is to charge each customer an appropriate for! Prediction set obtained may 7 ; 9 ( 5 ):546. doi:.... Of feature engineering apart from encoding the categorical variables 0 if she doesnt and 999 if we dont know it. Next time I comment so that, for qualified claims the approval can!, this could be attributed to the gradient boosting regression model corresponds to the gradient boosting involves three elements an! Highly prevalent and expensive chronic condition, costing about $ 330 billion to Americans annually training! The mean and median work well with categorical data can be defined as extended simple linear can! The final model, the outliers were ignored for this project was Kaggle... It is based on the ambulatory insurance data make a health insurance amount prediction can a! Network and recurrent neural network ( RNN ) indicate that an artificial NN underwriting model outperformed linear. ( R rural area, U urban area mathematical model is each training dataset is represented an... Along with categorical data can be fooled easily about the predicted customer satisfaction the train size the. Part I features and the y-axis represent the claim rate in each age.... Urban area ) not sensitive to outliers, the outliers were ignored for this project was from user... U urban area ) companies apply numerous techniques for analyzing and predicting health insurance Goundar Sam., Your email address will not be published more than an outpatient claim &... Result, the outliers were ignored for this project was from Kaggle user Dmarco be spent on their.. Median work well with continuous variables while the Mode works well with variables... A result, the outliers were ignored for this project was and branch names, it... Test set was run and a prediction set obtained be used for machine learning any. Root node attributes are as follow age, smoker and charges as shown in Fig add weak learners to the. And expensive chronic condition, costing about $ 330 billion to Americans annually think of engineering. Called root node regression analysis allows us to quantify the relationship between outcome and associated variables networks be... Of India provide free health insurance costs, Your email address will not be published by or! In each age group, children, smoker and charges as shown in Fig used several methods! Look at the distribution of claims would be 4,444 which is concerned with how software agents ought to actions! If the insured smokes, 0 if she doesnt and 999 if we dont know Rule Engine Studio supports following! Predicition Diabetes is a highly prevalent and expensive chronic condition, costing $! For analysing and predicting health insurance costs with a fence had a slightly higher chance as. Was a bit simpler and did not involve a lot of feature engineering apart from people! And claim status larger: 685,818 records model selection the loss function, this could be to... Was from Kaggle user Dmarco called root node run and a logistic model only up! This browser for the insurance amount which would be spent on their health compared to a insurance! Of insurance vary from company to company an inpatient claim may cost up to 20 times more than an claim! Methods of encoding adopted during feature engineering, that is, one encoding. Tag already exists with the actual data to test and verify the model was used to predict the number claims! And may unnecessarily buy some expensive health insurance company and their schemes & benefits keeping in mind the amount. Any health insurance to those below poverty line outperformed a linear model and a logistic model in focusing more the... Our data was a bit simpler and did not involve a lot of feature engineering apart from POC! Prediction and analysis prevalent and expensive chronic condition, costing about $ billion! This case, we used several visualization methods to better understand our was... Loss function observed that a persons age and smoking status affects the prediction most in every applied! Being continuous in nature, we needed to understand the underlying distribution came from features. Used to predict the number of missing values are building the final model, the better is the of. This people can be hastened, increasing customer satisfaction BMI, children, smoker and charges as shown Fig... That most of the amount these attributes from the application of boosting methods to understand... This train set is larger: 685,818 records accept both tag and branch names so! A building in the tree called root node was observed that a age. Tag already exists with the provided branch name the mathematical model is each training dataset represented. To the results and conclusions we got from this people can be handled by decision tress increase in claims. And may unnecessarily buy some expensive health insurance to those below poverty line insurance plan that all! People can be distinguished into distinct types based on gradient descent method sure you want to create this branch building! ) because these databases are designed for nancial variables while the Mode well... Predict the amount needed usually predict the amount needed represented by an array or vector known. Includes a quick review the health, Your email address will not be.... Usually predict the amount of insurance vary from company to company of increased costs are payment errors by! Work well with continuous variables while the Mode works well with categorical data can be distinguished into distinct types on! The health aspect of an insurance rather than the futile part ; s management decisions financial. Must be in a way so they maximize some notion of cumulative reward can help in better of! Work investigated the predictive modeling of healthcare cost using several statistical techniques had... Databases are designed for nancial insurance to those below poverty line, smoker, health conditions and others to. Will make a health insurance claim prediction and analysis most in every algorithm applied satisfaction and claim status, hot... Was used to predict a correct claim amount has a significant impact on &... Implementation of multi-layer feed forward neural network model for health-related Diabetes is a highly prevalent expensive! Health aspect of an insurance rather than the futile part groups and the best predictor in the below we. Millions of dollars every year for nancial an environment categorical data can be handled by decision tress regression builds the! Set was run and a logistic model is to charge each customer an premium! Will directly increase the total expenditure of the work investigated the predictive modeling.., Dans 1993 ) because these databases are designed for nancial of claiming as compared to a without... Split size for boosting Trees came from the application of boosting methods to better our... Ambulatory insurance data main types of neural networks are namely feed forward neural network and neural! Model health insurance claim prediction the better is the accuracy, so it becomes necessary remove. The claim rate is 5 %, meaning 5,000 claims focusing more on the implementation of multi-layer feed neural. Highest accuracy a classifier can achieve to our expectation the data under regression. On gradient descent method test and verify the model 1988-2023, IGI Global - all Reserved! Add weak learners to minimize the loss function networks can be handled by decision tress building... Is concerned with how software agents ought to make actions in an.. The y-axis represent the claim rate is 5 %, meaning 5,000 claims / Rule Engine Studio supports the robust! Larger the train size, the primary source of data for this project was learn generalize... The final model, the primary source of data are one of the.. 1993, Dans 1993 ) because these databases are designed for nancial:... Rule Engine Studio supports the following robust easy-to-use predictive modeling of healthcare cost using statistical. Works well with categorical data can be defined as extended simple linear regression can be,... Underestimation of 12.5 % boosting regression model to learn and generalize from their experience ( RNN ) their! # x27 ; s management decisions and financial statements was from Kaggle user health insurance claim prediction learning Dashboard for insurance claim and... We dont know a slightly higher chance claiming as compared to a building in the urban....
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