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This study aims to compare frequentist and Bayesian machine learning models to assess type 2 diabetes risk factors in different genders.

February 5, 2026

Gender-Based Comparative Study of Type 2 Diabetes Risk Factors in Kolkata, India: Frequentist versus Bayesian Approach in Machine Learning

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Comparative study evaluates risk factors for type 2 diabetes in Kolkata, suggesting implications for gender-specific healthcare.

This study aims to compare frequentist and Bayesian machine learning models to assess type 2 diabetes risk factors in different genders.
Utilized outpatient data from Belle Vue Clinic in Kolkata.
Employed frequentist models like Random Forest and Support Vector Classifier.
Implemented Bayesian models including Bayesian Additive Regression Trees and Relevance Vector Machine.
Analyzed factors like age, lifestyle, BMI, and waist-to-height ratio.
For males, BMI emerged as the most significant predictor of type 2 diabetes.
For females, waist-to-height ratio was identified as the key risk factor.
Highlighted differences in risk factors based on gender.
Demonstrated the effectiveness of both frequentist and Bayesian modeling approaches.

Jain et al. (2025) studied this question.

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