As a result, the median was chosen to replace the missing values. Here, our Machine Learning dashboard shows the claims types status. In the interest of this project and to gain more knowledge both encoding methodologies were used and the model evaluated for performance. The data was in structured format and was stores in a csv file format. DATASET USED The primary source of data for this project was . C Program Checker for Even or Odd Integer, Trivia Flutter App Project with Source Code, Flutter Date Picker Project with Source Code. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. The data has been imported from kaggle website. 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. arrow_right_alt. The size of the data used for training of data has a huge impact on the accuracy of data. Refresh the page, check. ), Goundar, Sam, et al. Model performance was compared using k-fold cross validation. Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. 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. Also with the characteristics we have to identify if the person will make a health insurance claim. 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. Well, no exactly. 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. In this paper, a method was developed, using large-scale health insurance claims data, to predict the number of hospitalization days in a population. 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. (2020). 4 shows the graphs of every single attribute taken as input to the gradient boosting regression model. 11.5s. Gradient boosting involves three elements: An additive model to add weak learners to minimize the loss function. 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). Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). In addition, only 0.5% of records in ambulatory and 0.1% records in surgery had 2 claims. The value of (health insurance) claims data in medical research has often been questioned (Jolins et al. 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. Health insurance is a necessity nowadays, and almost every individual is linked with a government or private health insurance company. In the past, research by Mahmoud et al. We are building the next-gen data science ecosystem https://www.analyticsvidhya.com. Accuracy defines the degree of correctness of the predicted value of the insurance amount. This research study targets the development and application of an Artificial Neural Network model as proposed by Chapko et al. 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. To do this we used box plots. Later the accuracies of these models were compared. Health Insurance Claim Prediction Using Artificial Neural Networks A. Bhardwaj Published 1 July 2020 Computer Science Int. 2 shows various machine learning types along with their properties. Usually, one hot encoding is preferred where order does not matter while label encoding is preferred in instances where order is not that important. Data. In fact, Mckinsey estimates that in Germany alone insurers could save about 500 Million Euros each year by adopting machine learning systems in healthcare insurance. The authors Motlagh et al. (2013) and Majhi (2018) on recurrent neural networks (RNNs) have also demonstrated that it is an improved forecasting model for time series. Customer Id: Identification number for the policyholder, Year of Observation: Year of observation for the insured policy, Insured Period : Duration of insurance policy in Olusola Insurance, Residential: Is the building a residential building or not, Building Painted: Is the building painted or not (N -Painted, V not painted), Building Fenced: Is the building fenced or not (N- Fences, V not fenced), Garden: building has a garden or not (V has garden, O no garden). How can enterprises effectively Adopt DevSecOps? https://www.moneycrashers.com/factors-health-insurance-premium- costs/, https://en.wikipedia.org/wiki/Healthcare_in_India, https://www.kaggle.com/mirichoi0218/insurance, https://economictimes.indiatimes.com/wealth/insure/what-you-need-to- know-before-buying-health- insurance/articleshow/47983447.cms?from=mdr, https://statistics.laerd.com/spss-tutorials/multiple-regression-using- spss-statistics.php, https://www.zdnet.com/article/the-true-costs-and-roi-of-implementing-, https://www.saedsayad.com/decision_tree_reg.htm, http://www.statsoft.com/Textbook/Boosting-Trees-Regression- Classification. Abhigna et al. 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. In the next blog well explain how we were able to achieve this goal. License. This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. Insurance companies apply numerous techniques for analysing and predicting health insurance costs. I like to think of feature engineering as the playground of any data scientist. The model was used to predict the insurance amount which would be spent on their health. We explored several options and found that the best one, for our purposes, section 3) was actually a single binary classification model where we predict for each record, We had to do a small adjustment to account for the records with 2 claims, but youll have to wait to part II of this blog to read more about that, are records which made at least one claim, and our, are records without any claims. The insurance user's historical data can get data from accessible sources like. can Streamline Data Operations and enable 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. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. Achieve Unified Customer Experience with efficient and intelligent insight-driven solutions. 11.5 second run - successful. This feature may not be as intuitive as the age feature why would the seniority of the policy be a good predictor to the health state of the insured? Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. The final model was obtained using Grid Search Cross Validation. Health Insurance Cost Predicition. 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. Then the predicted amount was compared with the actual data to test and verify the model. Our data was a bit simpler and did not involve a lot of feature engineering apart from encoding the categorical variables. "Health Insurance Claim Prediction Using Artificial Neural Networks.". Later they can comply with any health insurance company and their schemes & benefits keeping in mind the predicted amount from our project. (2017) state that artificial neural network (ANN) has been constructed on the human brain structure with very useful and effective pattern classification capabilities. Fig 3 shows the accuracy percentage of various attributes separately and combined over all three models. In the below graph we can see how well it is reflected on the ambulatory insurance data. BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. 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. Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. (2022). Neural networks can be distinguished into distinct types based on the architecture. Accurate prediction gives a chance to reduce financial loss for the company. Machine learning can be defined as the process of teaching a computer system which allows it to make accurate predictions after the data is fed. Approach : Pre . Using a series of machine learning algorithms, this study provides a computational intelligence approach for predicting healthcare insurance costs. A tag already exists with the provided branch name. Description. Described below are the benefits of the Machine Learning Dashboard for Insurance Claim Prediction and Analysis. There are many techniques to handle imbalanced data sets. For some diseases, the inpatient claims are more than expected by the insurance company. HEALTH_INSURANCE_CLAIM_PREDICTION. Supervised learning algorithms create a mathematical model according to a set of data that contains both the inputs and the desired outputs. Dong et al. It was gathered that multiple linear regression and gradient boosting algorithms performed better than the linear regression and decision tree. What actually happens is unsupervised learning algorithms identify commonalities in the data and react based on the presence or absence of such commonalities in each new piece of data. Appl. A matrix is used for the representation of training data. effective Management. (2016), neural network is very similar to biological neural networks. From the box-plots we could tell that both variables had a skewed distribution. Where a person can ensure that the amount he/she is going to opt is justified. 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. Here, our Machine Learning dashboard shows the claims types status. 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. Key Elements for a Successful Cloud Migration? You signed in with another tab or window. The mean and median work well with continuous variables while the Mode works well with categorical variables. Factors determining the amount of insurance vary from company to company. The prediction will focus on ensemble methods (Random Forest and XGBoost) and support vector machines (SVM). 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. i.e. Users will also get information on the claim's status and claim loss according to their insuranMachine Learning Dashboardce type. Creativity and domain expertise come into play in this area. Though unsupervised learning, encompasses other domains involving summarizing and explaining data features also. Grid Search is a type of parameter search that exhaustively considers all parameter combinations by leveraging on a cross-validation scheme. And those are good metrics to evaluate models with. 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. 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. Take for example the, feature. 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. 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. 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. 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