Gender-Based Comparative Study of Type 2 Diabetes Risk Factors in
  Kolkata, India: A Machine Learning Approach
Rahul Jain Anoushka Saha Gourav Daga Durba Bhattacharya Madhura Das Gupta Sourav Chowdhury Suparna Roychowdhury
Type 2 diabetes mellitus represents a prevalent and widespread global health
concern, necessitating a comprehensive assessment of its risk factors. This
study aimed towards learning whether there is any differential impact of age,
Lifestyle, BMI and Waist to height ratio on the risk of Type 2 diabetes
mellitus in males and females in Kolkata, West Bengal, India based on a sample
observed from the out-patient consultation department of Belle Vue Clinic in
Kolkata. Various machine learning models like Logistic Regression, Random
Forest, and Support Vector Classifier, were used to predict the risk of
diabetes, and performance was compared based on different predictors. Our
findings indicate a significant age-related increase in risk of diabetes for
both males and females. Although exercising and BMI was found to have
significant impact on the risk of Type 2 diabetes in males, in females both
turned out to be statistically insignificant. For both males and females,
predictive models based on WhtR demonstrated superior performance in risk
assessment compared to those based on BMI. This study sheds light on the
gender-specific differences in the risk factors for Type 2 diabetes, offering
valuable insights that can be used towards more targeted healthcare
interventions and public health strategies.