Prediction of Satisfaction Accuracy on Health Insurance Policy through Machine Learning Algorithms Especially Tree Models
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Abstract
This study was to predict the satisfaction accuracy on health insurance policy (HIP) among participants through machine learning (ML) algorithms especially tree models. We studied the data mining based on ML classifiers by using WEKA tool, version, 3.8.5. The study was conducted through questionnaires-based survey among 385 respondents of eastern India. The predictive accuracy of data of satisfaction on HIP through ML algorithms especially 4 tree algorithms viz. decision tree (DT) J48, Random forest (RF), Random tree (RT) and Fast decision tree learner tree (REPT) along with 9 attributes viz. Facilities, Claim_coverage, Tax_benefit, Unexceptional_risk, Trust_insurer, Policy_benefit_bonus, Policy_benefit_ premium_amount, Amount_claim_offered_maturity, Policy_benefit_family_production and class (poor, moderate and good response) from dataset were determined. In our study, in the poor class a maximum value of precision recall curve (PRC) value was obtained as per the ML algorithms such as RF (96%) and RT (93%) followed by REPT (91%) and DT J48 (90%). It is concluded that the valuable information of the dataset through ML algorithms especially tree models are obtained prediction accuracy higher in RF and RT and lower in REPT and DTJ48 algorithms as per cross validation (CV) test. From this study, it was predicted poor satisfaction on HIP among participants. It is suggested to validate the present predictive data.
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References
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