Attributes which had no effect on the prediction were removed from the features. Prediction is premature and does not comply with any particular company so it must not be only criteria in selection of a health insurance. It is based on a knowledge based challenge posted on the Zindi platform based on the Olusola Insurance Company. "Health Insurance Claim Prediction Using Artificial Neural Networks.". All Rights Reserved. This amount needs to be included in the yearly financial budgets. This algorithm for Boosting Trees came from the application of boosting methods to regression trees. Also people in rural areas are unaware of the fact that the government of India provide free health insurance to those below poverty line. Users can develop insurance claims prediction models with the help of intuitive model visualization tools. trend was observed for the surgery data). 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. It was observed that a persons age and smoking status affects the prediction most in every algorithm applied. 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. Other two regression models also gave good accuracies about 80% In their prediction. Health Insurance Claim Prediction Using Artificial Neural Networks. 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. And its also not even the main issue. 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 Your email address will not be published. Logs. Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). Example, Sangwan et al. 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. Approach : Pre . (2017) state that artificial neural network (ANN) has been constructed on the human brain structure with very useful and effective pattern classification capabilities. Logs. According to Zhang et al. The larger the train size, the better is the accuracy. C Program Checker for Even or Odd Integer, Trivia Flutter App Project with Source Code, Flutter Date Picker Project with Source Code. (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. Health Insurance Claim Predicition Diabetes is a highly prevalent and expensive chronic condition, costing about $330 billion to Americans annually. Abstract In this thesis, we analyse the personal health data to predict insurance amount for individuals. According to Rizal et al. This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. (2022). Last modified January 29, 2019, Your email address will not be published. This is the field you are asked to predict in the test set. 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. An inpatient claim may cost up to 20 times more than an outpatient claim. There are many techniques to handle imbalanced data sets. age : age of policyholder sex: gender of policy holder (female=0, male=1) Where a person can ensure that the amount he/she is going to opt is justified. How can enterprises effectively Adopt DevSecOps? Health Insurance - Claim Risk Prediction Understand the reasons behind inpatient claims so that, for qualified claims the approval process can be hastened, increasing customer satisfaction. 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. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Creativity and domain expertise come into play in this area. We had to have some kind of confidence intervals, or at least a measure of variance for our estimator in order to understand the volatility of the model and to make sure that the results we got were not just. This Notebook has been released under the Apache 2.0 open source license. Once training data is in a suitable form to feed to the model, the training and testing phase of the model can proceed. 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. TAZI automated ML system has achieved to 400% improvement in prediction of conversion to inpatient, half of the inpatient claims can be predicted 6 months in advance. Later the accuracies of these models were compared. "Health Insurance Claim Prediction Using Artificial Neural Networks,", Health Insurance Claim Prediction Using Artificial Neural Networks, 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, Computer Science and IT Knowledge Solutions e-Journal Collection, Business Knowledge Solutions e-Journal Collection, International Journal of System Dynamics Applications (IJSDA). Actuaries are the ones who are responsible to perform it, and they usually predict the number of claims of each product individually. Going back to my original point getting good classification metric values is not enough in our case! By filtering and various machine learning models accuracy can be improved. Goundar, Sam, et al. Introduction to Digital Platform Strategy? The size of the data used for training of data has a huge impact on the accuracy of data. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. (2013) and Majhi (2018) on recurrent neural networks (RNNs) have also demonstrated that it is an improved forecasting model for time series. We already say how a. model can achieve 97% accuracy on our data. This can help a person in focusing more on the health aspect of an insurance rather than the futile part. The model predicted the accuracy of model by using different algorithms, different features and different train test split size. Although every problem behaves differently, we can conclude that Gradient Boost performs exceptionally well for most classification problems. 1 input and 0 output. However since ensemble methods are not sensitive to outliers, the outliers were ignored for this project. 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. Achieve Unified Customer Experience with efficient and intelligent insight-driven solutions. (2020). However, it is. Health insurers offer coverage and policies for various products, such as ambulatory, surgery, personal accidents, severe illness, transplants and much more. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. In I. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. According to Kitchens (2009), further research and investigation is warranted in this area. According to Rizal et al. Appl. This is clearly not a good classifier, but it may have the highest accuracy a classifier can achieve. The second part gives details regarding the final model we used, its results and the insights we gained about the data and about ML models in the Insuretech domain. 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. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 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. 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. The data was imported using pandas library. Reinforcement learning is class of machine learning which is concerned with how software agents ought to make actions in an environment. These claim amounts are usually high in millions of dollars every year. The authors Motlagh et al. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. License. These decision nodes have two or more branches, each representing values for the attribute tested. 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. 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. Take for example the, feature. Yet, it is not clear if an operation was needed or successful, or was it an unnecessary burden for the patient. 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 tag already exists with the provided branch name. 1. 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. According to our dataset, age and smoking status has the maximum impact on the amount prediction with smoker being the one attribute with maximum effect. Fig. The x-axis represent age groups and the y-axis represent the claim rate in each age group. It helps in spotting patterns, detecting anomalies or outliers and discovering patterns. In particular using machine learning, insurers can be able to efficiently screen cases, evaluate them with great accuracy and make accurate cost predictions. 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. Refresh the page, check. 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. 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. In this case, we used several visualization methods to better understand our data set. Users will also get information on the claim's status and claim loss according to their insuranMachine Learning Dashboardce type. 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. In the next part of this blog well finally get to the modeling process! A decision tree with decision nodes and leaf nodes is obtained as a final result. You signed in with another tab or window. According to Zhang et al. 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. A building without a fence had a slightly higher chance of claiming as compared to a building with a fence. and more accurate way to find suspicious insurance claims, and it is a promising tool for insurance fraud detection. Numerical data along with categorical data can be handled by decision tress. 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. Health insurance is a necessity nowadays, and almost every individual is linked with a government or private health insurance company. 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. The real-world data is noisy, incomplete and inconsistent. Machine Learning for Insurance Claim Prediction | Complete ML Model. 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. Currently utilizing existing or traditional methods of forecasting with variance. Most of the cost is attributed to the 'type-2' version of diabetes, which is typically diagnosed in middle age. needed. 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. Dyn. Insurance Claim Prediction Using Machine Learning Ensemble Classifier | by Paul Wanyanga | Analytics Vidhya | Medium 500 Apologies, but something went wrong on our end. 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. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Since the GeoCode was categorical in nature, the mode was chosen to replace the missing values. 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. Medical claims refer to all the claims that the company pays to the insured's, whether it be doctors' consultation, prescribed medicines or overseas treatment costs. The model was used to predict the insurance amount which would be spent on their health. The topmost decision node corresponds to the best predictor in the tree called root node. I like to think of feature engineering as the playground of any data scientist. Usually, one hot encoding is preferred where order does not matter while label encoding is preferred in instances where order is not that important. 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. 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 . The dataset is comprised of 1338 records with 6 attributes. Using this approach, a best model was derived with an accuracy of 0.79. 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. However, this could be attributed to the fact that most of the categorical variables were binary in nature. 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. The models can be applied to the data collected in coming years to predict the premium. Example, Sangwan et al. (R rural area, U urban area). You signed in with another tab or window. Decision on the numerical target is represented by leaf node. 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. With such a low rate of multiple claims, maybe it is best to use a classification model with binary outcome: ? The effect of various independent variables on the premium amount was also checked. The network was trained using immediate past 12 years of medical yearly claims data. Neural networks can be distinguished into distinct types based on the architecture. In this learning, algorithms take a set of data that contains only inputs, and find structure in the data, like grouping or clustering of data points. In a dataset not every attribute has an impact on the prediction. A matrix is used for the representation of training data. Copyright 1988-2023, IGI Global - All Rights Reserved, Goundar, Sam, et al. (2011) and El-said et al. (2020) proposed artificial neural network is commonly utilized by organizations for forecasting bankruptcy, customer churning, stock price forecasting and in many other applications and areas. In the insurance business, two things are considered when analysing losses: frequency of loss and severity of loss. In the next blog well explain how we were able to achieve this goal. A tag already exists with the provided branch name. Random Forest Model gave an R^2 score value of 0.83. Machine Learning approach is also used for predicting high-cost expenditures in health care. (2013) and Majhi (2018) on recurrent neural networks (RNNs) have also demonstrated that it is an improved forecasting model for time series. Factors determining the amount of insurance vary from company to company.