Health Insurance Claim Prediction Using Artificial Neural Networks. This involves choosing the best modelling approach for the task, or the best parameter settings for a given model. 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. (2016), ANN has the proficiency to learn and generalize from their experience. In fact, the term model selection often refers to both of these processes, as, in many cases, various models were tried first and best performing model (with the best performing parameter settings for each model) was selected. Are you sure you want to create this branch? Insurance Companies apply numerous models for analyzing and predicting health insurance cost. Keywords Regression, Premium, Machine Learning. Insurance Claims Risk Predictive Analytics and Software Tools. Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. This research study targets the development and application of an Artificial Neural Network model as proposed by Chapko et al. Here, our Machine Learning dashboard shows the claims types status. 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. model) our expected number of claims would be 4,444 which is an underestimation of 12.5%. In addition, only 0.5% of records in ambulatory and 0.1% records in surgery had 2 claims. This amount needs to be included in the yearly financial budgets. The network was trained using immediate past 12 years of medical yearly claims data. 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. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. i.e. Comments (7) Run. (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. To do this we used box plots. 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. 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. The real-world data is noisy, incomplete and inconsistent. Key Elements for a Successful Cloud Migration? These claim amounts are usually high in millions of dollars every year. 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. 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). Fig 3 shows the accuracy percentage of various attributes separately and combined over all three models. Also it can provide an idea about gaining extra benefits from the health insurance. Regression or classification models in decision tree regression builds in the form of a tree structure. This is the field you are asked to predict in the test set. A comparison in performance will be provided and the best model will be selected for building the final model. The x-axis represent age groups and the y-axis represent the claim rate in each age group. Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). 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. (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. (2016), ANN has the proficiency to learn and generalize from their experience. And those are good metrics to evaluate models with. 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. We are building the next-gen data science ecosystem https://www.analyticsvidhya.com. (2011) and El-said et al. Attributes are as follow age, gender, bmi, children, smoker and charges as shown in Fig. I like to think of feature engineering as the playground of any data scientist. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. Factors determining the amount of insurance vary from company to company. If you have some experience in Machine Learning and Data Science you might be asking yourself, so we need to predict for each policy how many claims it will make. 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. Attributes which had no effect on the prediction were removed from the features. According to Rizal et al. can Streamline Data Operations and enable Removing such attributes not only help in improving accuracy but also the overall performance and speed. 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. 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. Luckily for us, using a relatively simple one like under-sampling did the trick and solved our problem. (2017) state that artificial neural network (ANN) has been constructed on the human brain structure with very useful and effective pattern classification capabilities. 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. $$Recall= \frac{True\: positive}{All\: positives} = 0.9 \rightarrow \frac{True\: positive}{5,000} = 0.9 \rightarrow True\: positive = 0.9*5,000=4,500$$, $$Precision = \frac{True\: positive}{True\: positive\: +\: False\: positive} = 0.8 \rightarrow \frac{4,500}{4,500\:+\:False\: positive} = 0.8 \rightarrow False\: positive = 1,125$$, 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 this paper, a method was developed, using large-scale health insurance claims data, to predict the number of hospitalization days in a population. Results indicate that an artificial NN underwriting model outperformed a linear model and a logistic model. Though unsupervised learning, encompasses other domains involving summarizing and explaining data features also. Insurance Claim Prediction Using Machine Learning Ensemble Classifier | by Paul Wanyanga | Analytics Vidhya | Medium 500 Apologies, but something went wrong on our end. A tag already exists with the provided branch name. 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. 1993, Dans 1993) because these databases are designed for nancial . It was gathered that multiple linear regression and gradient boosting algorithms performed better than the linear regression and decision tree. Insurance Claim Prediction Problem Statement A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. A building without a garden had a slightly higher chance of claiming as compared to a building with a garden. 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. 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. Currently utilizing existing or traditional methods of forecasting with variance. Users can develop insurance claims prediction models with the help of intuitive model visualization tools. Health Insurance Claim Prediction Using Artificial Neural Networks. The effect of various independent variables on the premium amount was also checked. Reinforcement learning is class of machine learning which is concerned with how software agents ought to make actions in an environment. Adapt to new evolving tech stack solutions to ensure informed business decisions. Machine Learning for Insurance Claim Prediction | Complete ML Model. These claim amounts are usually high in millions of dollars every year. 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. As a result, we have given a demo of dashboards for reference; you will be confident in incurred loss and claim status as a predicted model. This amount needs to be included in Dataset was used for training the models and that training helped to come up with some predictions. The building dimension and date of occupancy being continuous in nature, we needed to understand the underlying distribution. Take for example the, feature. This Notebook has been released under the Apache 2.0 open source license. Data. The larger the train size, the better is the accuracy. These inconsistencies must be removed before doing any analysis on data. The website provides with a variety of data and the data used for the project is an insurance amount data. REFERENCES Whats happening in the mathematical model is each training dataset is represented by an array or vector, known as a feature vector. Each plan has its own predefined . Decision on the numerical target is represented by leaf node. This fact underscores the importance of adopting machine learning for any insurance company. It also shows the premium status and customer satisfaction every . Creativity and domain expertise come into play in this area. 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