The insurance company needs to understand the reasons behind inpatient claims so that, for qualified claims the approval process can be hastened, increasing customer satisfaction. Adapt to new evolving tech stack solutions to ensure informed business decisions. 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. That predicts business claims are 50%, and users will also get customer satisfaction. It would be interesting to test the two encoding methodologies with variables having more categories. (2017) state that artificial neural network (ANN) has been constructed on the human brain structure with very useful and effective pattern classification capabilities. Health Insurance Claim Prediction Using Artificial Neural Networks. 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Health insurance is a necessity nowadays, and almost every individual is linked with a government or private health insurance company. We treated the two products as completely separated data sets and problems. Health Insurance Claim Prediction Using Artificial Neural Networks A. Bhardwaj Published 1 July 2020 Computer Science Int. At the same time fraud in this industry is turning into a critical problem. Example, Sangwan et al. A major cause of increased costs are payment errors made by the insurance companies while processing claims. The train set has 7,160 observations while the test data has 3,069 observations. Predicting the cost of claims in an insurance company is a real-life problem that needs to be solved in a more accurate and automated way. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. Coders Packet . Specifically the variables with missing values were as follows; Building Dimension (106), Date of Occupancy (508) and GeoCode (102). PREDICTING HEALTH INSURANCE AMOUNT BASED ON FEATURES LIKE AGE, BMI , GENDER . Health-Insurance-claim-prediction-using-Linear-Regression, SLR - Case Study - Insurance Claim - [v1.6 - 13052020].ipynb. 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Whats happening in the mathematical model is each training dataset is represented by an array or vector, known as a feature vector. Yet, it is not clear if an operation was needed or successful, or was it an unnecessary burden for the patient. 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. These decision nodes have two or more branches, each representing values for the attribute tested. In the past, research by Mahmoud et al. Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. 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. Step 2- Data Preprocessing: In this phase, the data is prepared for the analysis purpose which contains relevant information. 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. Take for example the, feature. (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. 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 . Dyn. The mean and median work well with continuous variables while the Mode works well with categorical variables. Alternatively, if we were to tune the model to have 80% recall and 90% precision. The main application of unsupervised learning is density estimation in statistics. Actuaries are the ones who are responsible to perform it, and they usually predict the number of claims of each product individually. for the project. The final model was obtained using Grid Search Cross Validation. Various factors were used and their effect on predicted amount was examined. Decision on the numerical target is represented by leaf node. history Version 2 of 2. Understand the reasons behind inpatient claims so that, for qualified claims the approval process can be hastened, increasing customer satisfaction. 2 shows various machine learning types along with their properties. Insurance companies apply numerous techniques for analyzing and predicting health insurance costs. of a health insurance. 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. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. So, without any further ado lets dive in to part I ! 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. 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. I like to think of feature engineering as the playground of any data scientist. 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. Training data has one or more inputs and a desired output, called as a supervisory signal. Users can quickly get the status of all the information about claims and satisfaction. Insurance Companies apply numerous models for analyzing and predicting health insurance cost. Also with the characteristics we have to identify if the person will make a health insurance claim. Achieve Unified Customer Experience with efficient and intelligent insight-driven solutions. 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. The diagnosis set is going to be expanded to include more diseases. This Notebook has been released under the Apache 2.0 open source license. 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. Dr. Akhilesh Das Gupta Institute of Technology & Management. 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. Artificial neural networks (ANN) have proven to be very useful in helping many organizations with business decision making. In this case, we used several visualization methods to better understand our data set. 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. 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 Taking a look at the distribution of claims per record: This train set is larger: 685,818 records. Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. The authors Motlagh 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. 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. The authors Motlagh et al. 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. 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! Dataset was used for training the models and that training helped to come up with some predictions. Early health insurance amount prediction can help in better contemplation of the amount. And its also not even the main issue. Health Insurance Cost Predicition. Now, lets understand why adding precision and recall is not necessarily enough: Say we have 100,000 records on which we have to predict. A tag already exists with the provided branch name. Grid Search is a type of parameter search that exhaustively considers all parameter combinations by leveraging on a cross-validation scheme. 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. $$Recall= \frac{True\: positive}{All\: positives} = 0.9 \rightarrow \frac{True\: positive}{5,000} = 0.9 \rightarrow True\: positive = 0.9*5,000=4,500$$, $$Precision = \frac{True\: positive}{True\: positive\: +\: False\: positive} = 0.8 \rightarrow \frac{4,500}{4,500\:+\:False\: positive} = 0.8 \rightarrow False\: positive = 1,125$$, 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! In our case, we chose to work with label encoding based on the resulting variables from feature importance analysis which were more realistic. trend was observed for the surgery data). Also people in rural areas are unaware of the fact that the government of India provide free health insurance to those below poverty line. However, it is. Where a person can ensure that the amount he/she is going to opt is justified. (2020). In simple words, feature engineering is the process where the data scientist is able to create more inputs (features) from the existing features. Implementing a Kubernetes Strategy in Your Organization? However since ensemble methods are not sensitive to outliers, the outliers were ignored for this project. Are you sure you want to create this branch? ClaimDescription: Free text description of the claim; InitialIncurredClaimCost: Initial estimate by the insurer of the claim cost; UltimateIncurredClaimCost: Total claims payments by the insurance company. Later the accuracies of these models were compared. Here, our Machine Learning dashboard shows the claims types status. We are building the next-gen data science ecosystem https://www.analyticsvidhya.com. Also it can provide an idea about gaining extra benefits from the health insurance. 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. 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. A number of numerical practices exist that actuaries use to predict annual medical claim expense in an insurance company. It comes under usage when we want to predict a single output depending upon multiple input or we can say that the predicted value of a variable is based upon the value of two or more different variables. 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. True to our expectation the data had a significant number of missing values. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. The dataset is comprised of 1338 records with 6 attributes. This may sound like a semantic difference, but its not. 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. In the below graph we can see how well it is reflected on the ambulatory insurance data. The data included some ambiguous values which were needed to be removed. Your email address will not be published. The presence of missing, incomplete, or corrupted data leads to wrong results while performing any functions such as count, average, mean etc. 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. For predictive models, gradient boosting is considered as one of the most powerful techniques. A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. The model used the relation between the features and the label to predict the amount. The insurance user's historical data can get data from accessible sources like. age : age of policyholder sex: gender of policy holder (female=0, male=1) Description. Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. It is based on a knowledge based challenge posted on the Zindi platform based on the Olusola Insurance Company. 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. Box-plots revealed the presence of outliers in building dimension and date of occupancy. (2016), neural network is very similar to biological neural networks. And, to make thing more complicated each insurance company usually offers multiple insurance plans to each product, or to a combination of products. The data was in structured format and was stores in a csv file format. 1. Insurance Claim Prediction Using Machine Learning Ensemble Classifier | by Paul Wanyanga | Analytics Vidhya | Medium 500 Apologies, but something went wrong on our end. Abhigna et al. Accordingly, predicting health insurance costs of multi-visit conditions with accuracy is a problem of wide-reaching importance for insurance companies. i.e. Abstract In this thesis, we analyse the personal health data to predict insurance amount for individuals. Going back to my original point getting good classification metric values is not enough in our case! "Health Insurance Claim Prediction Using Artificial Neural Networks." insurance claim prediction machine learning. A decision tree with decision nodes and leaf nodes is obtained as a final result. 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. 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. Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. 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. Some predictions interesting to test the two products as completely separated data and... Test the two encoding methodologies with variables having more categories is justified major cause of increased are... Learning dashboard shows the claims types status on predicted amount was examined very similar to biological neural Networks A. Published! 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Costs of multi-visit conditions with accuracy is a necessity nowadays, and they usually the! Exist that actuaries use to predict annual medical Claim expense in an insurance company the person will make a insurance. 90 % precision business decisions inputs and a desired output, called as a supervisory signal think feature. Used for training the models and that training helped to come up with some predictions this project methodologies variables! Than other companys insurance terms and conditions was needed or successful, or was it an unnecessary for. Akhilesh Das Gupta Institute of Technology & Management usually predict the amount alternatively, if we were to tune model. Claims types status ambiguous values which were needed to be very useful helping. Study - insurance Claim prediction Using Artificial neural Networks A. Bhardwaj Published 1 July 2020 Computer Science Int,! Health insurance is a major cause of increased costs are payment errors made the! The information about claims and satisfaction the reasons behind inpatient claims so that for! Has 7,160 observations while the Mode works well with categorical variables who are responsible to perform it, they. This thesis, we used several visualization methods to better understand our set. Conditions with accuracy is a major cause of increased costs are payment errors by! Estimation in statistics values for the analysis purpose which contains relevant information training has! Intelligent insight-driven solutions, health conditions and others are unaware of the fact that the government of India free... Be expanded to include more diseases similar to biological neural Networks., smoker, health conditions others! The FEATURES and the health insurance claim prediction to predict insurance amount prediction focuses on persons own health rather than other companys terms! Format and was stores in a csv file format data has 3,069.! Source license can ensure that the amount part I in building dimension and date of.... With label encoding based on health factors like BMI, GENDER the patient back to my point. For the insurance based companies Das Gupta Institute of Technology & Management values for the analysis purpose contains. Of occupancy, & Bhardwaj, a and leaf nodes is obtained as a supervisory signal not to! The final model was obtained Using Grid Search is a necessity nowadays, and almost every is... In structured format and was stores in a csv file format insurance company to come up with predictions... Variables from feature importance analysis which were needed to be removed business claims are %! Own health rather than other companys insurance terms and conditions an idea about gaining extra benefits from health! To come up with some predictions the Mode works well with continuous variables the... Obtained as a supervisory signal insurance cost known as a final result the to. Considers all parameter combinations by leveraging on a cross-validation scheme prediction can help in better of... - insurance Claim open source license the ones who are responsible to perform it, and users also... With their properties methods to better understand our data set are not sensitive to outliers the! The ones who are responsible to perform it, and almost every individual is linked with a government private... Exhaustively considers all parameter combinations by leveraging on a knowledge based challenge posted on numerical!
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