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All Journal Indonesian Journal for Health Sciences (IJHS)
Lucetta, Aqilla Dheanya
Unknown Affiliation
Nursing Public Health

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APPLICATION OF LOGISTIC REGRESSION FOR HEALTH INSURANCE PREMIUM CLASSIFICATION Tantono, Celia Christy Merlinda; Lucetta, Aqilla Dheanya; Felicia, Putri; Nugraha, Edwin Setiawan
Indonesian Journal of Health Science Vol 10 No 1 (2026): March
Publisher : Universitas Muhammadiyah Ponorogo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24269/ijhs.v10i1.12271

Abstract

This study analyzes individual health insurance data using logistic regression to classify premiums into high and low categories based on ten medical and demographic predictors. By transforming a continuous premium variable into binary classes, high and low premiums, this research evaluates the significant factors influencing premium pricing decisions. Logistic regression was selected for its ability to model binary outcomes and estimate the likelihood that the customer belongs to the high premium category. To evaluate the significance of the predictors and the overall model fit, the Likelihood Ratio Test and Wald test were performed, identifying Age  and Weight  as significant predictors affecting premium classification. The final logistic regression model has an excellent ability to predict, given the area under the curve (AUC) of 0.97 and a 95% accuracy. These results show how well logistic regression works to enhance risk classification and support data driven policy modifications in insurance underwriting procedures.
Co-Authors Felicia, Putri Nugraha, Edwin Setiawan Tantono, Celia Christy Merlinda