Random Forest Model gave an R^2 score value of 0.83. (2016), ANN has the proficiency to learn and generalize from their experience. Using the final model, the test set was run and a prediction set obtained. The insurance user's historical data can get data from accessible sources like. Once training data is in a suitable form to feed to the model, the training and testing phase of the model can proceed. Artificial neural networks (ANN) have proven to be very useful in helping many organizations with business decision making. an insurance plan that cover all ambulatory needs and emergency surgery only, up to $20,000). 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. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. trend was observed for the surgery data). Numerical data along with categorical data can be handled by decision tress. Figure 4: Attributes vs Prediction Graphs Gradient Boosting Regression. Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. 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. 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%. Later the accuracies of these models were compared. This is the field you are asked to predict in the test set. These claim amounts are usually high in millions of dollars every year. 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. Understand the reasons behind inpatient claims so that, for qualified claims the approval process can be hastened, increasing customer satisfaction. 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Fig 3 shows the accuracy percentage of various attributes separately and combined over all three models. This amount needs to be included in the yearly financial budgets. For some diseases, the inpatient claims are more than expected by the insurance company. Key Elements for a Successful Cloud Migration? 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! Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. 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. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. 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. 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). (2011) and El-said et al. An increase in medical claims will directly increase the total expenditure of the company thus affects the profit margin. Required fields are marked *. A building without a garden had a slightly higher chance of claiming as compared to a building with a garden. In the next part of this blog well finally get to the modeling process! Accordingly, predicting health insurance costs of multi-visit conditions with accuracy is a problem of wide-reaching importance for insurance companies. . 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. In I. In addition, only 0.5% of records in ambulatory and 0.1% records in surgery had 2 claims. for example). Adapt to new evolving tech stack solutions to ensure informed business decisions. 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. (2013) and Majhi (2018) on recurrent neural networks (RNNs) have also demonstrated that it is an improved forecasting model for time series. 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. Imbalanced data sets are a known problem in ML and can harm the quality of prediction, especially if one is trying to optimize the, is defined as the fraction of correctly predicted outcomes out of the entire prediction vector. 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. needed. 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. (2016), neural network is very similar to biological neural networks. The final model was obtained using Grid Search Cross Validation. 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. It is based on a knowledge based challenge posted on the Zindi platform based on the Olusola Insurance Company. The diagnosis set is going to be expanded to include more diseases. An inpatient claim may cost up to 20 times more than an outpatient claim. Luckily for us, using a relatively simple one like under-sampling did the trick and solved our problem. 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. Prediction is premature and does not comply with any particular company so it must not be only criteria in selection of a health insurance. Training data has one or more inputs and a desired output, called as a supervisory signal. Described below are the benefits of the Machine Learning Dashboard for Insurance Claim Prediction and Analysis. The data included various attributes such as age, gender, body mass index, smoker and the charges attribute which will work as the label. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. The network was trained using immediate past 12 years of medical yearly claims data. Dataset was used for training the models and that training helped to come up with some predictions. The model proposed in this study could be a useful tool for policymakers in predicting the trends of CKD in the population. Appl. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. Data. A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. Required fields are marked *. of a health insurance. We are building the next-gen data science ecosystem https://www.analyticsvidhya.com. 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Also it can provide an idea about gaining extra benefits from the health insurance. Health Insurance Claim Prediction Using Artificial Neural Networks. This research focusses on the implementation of multi-layer feed forward neural network with back propagation algorithm based on gradient descent method. All Rights Reserved. HEALTH_INSURANCE_CLAIM_PREDICTION. As a result, the median was chosen to replace the missing values. (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. document.getElementById( "ak_js_1" ).setAttribute( "value", ( new Date() ).getTime() ); Follow Tutorials 2022. C Program Checker for Even or Odd Integer, Trivia Flutter App Project with Source Code, Flutter Date Picker Project with Source Code. 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. It helps in spotting patterns, detecting anomalies or outliers and discovering patterns. Neural networks can be distinguished into distinct types based on the architecture. Settlement: Area where the building is located. Neural networks can be distinguished into distinct types based on the architecture. Insurance Claim Prediction Using Machine Learning Ensemble Classifier | by Paul Wanyanga | Analytics Vidhya | Medium 500 Apologies, but something went wrong on our end. On the other hand, the maximum number of claims per year is bound by 2 so we dont want to predict more than that and no regression model can give us such a grantee. The goal of this project is to allows a person to get an idea about the necessary amount required according to their own health status. Now, if we look at the claim rate in each smoking group using this simple two-way frequency table we see little differences between groups, which means we can assume that this feature is not going to be a very strong predictor: So, we have the data for both products, we created some features, and at least some of them seem promising in their prediction abilities looks like we are ready to start modeling, right? 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. Taking a look at the distribution of claims per record: This train set is larger: 685,818 records. This fact underscores the importance of adopting machine learning for any insurance company. 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. In the past, research by Mahmoud et al. Users can develop insurance claims prediction models with the help of intuitive model visualization tools. Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). and more accurate way to find suspicious insurance claims, and it is a promising tool for insurance fraud detection. In a dataset not every attribute has an impact on the prediction. The data included some ambiguous values which were needed to be removed. 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. Then the predicted amount was compared with the actual data to test and verify the model. Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. Currently utilizing existing or traditional methods of forecasting with variance. All Rights Reserved. Also it can provide an idea about gaining extra benefits from the health insurance. Specifically the variables with missing values were as follows; Building Dimension (106), Date of Occupancy (508) and GeoCode (102). Leverage the True potential of AI-driven implementation to streamline the development of applications. 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. 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. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. You signed in with another tab or window. According to Zhang et al. Comments (7) Run. The mean and median work well with continuous variables while the Mode works well with categorical variables. Abstract In this thesis, we analyse the personal health data to predict insurance amount for individuals. Figure 1: Sample of Health Insurance Dataset. The data was in structured format and was stores in a csv file format. Notebook. model) our expected number of claims would be 4,444 which is an underestimation of 12.5%. 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 real-world data is noisy, incomplete and inconsistent. 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. Accurate prediction gives a chance to reduce financial loss for the company. 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 Recurrent neural network and recurrent neural network with back propagation algorithm based on health factors like BMI,,! Be 4,444 which is an underestimation of 12.5 % some ambiguous values which were needed to very! Learning Dashboard for insurance companies needs and emergency surgery only, up to times! 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