Chauhan, Kirti, Sathyapalan, Thozhukat, Malabu, Usman, Joshi, Shashank Rameshchandra, Singh, Shri Kant, and Deshmukh, Harshal (2025) Machine learning identifies waist-height ratio (WHtR) as the strongest determinant of diabetes and prediabetes in children and adolescents: A comprehensive national nutrition survey. International Journal of Diabetes in Developing Countries. (In Press)

Preview

PDF (Published Version) - Published Version Available under License Creative Commons Attribution. Download (1MB) | Preview

Abstract

Background: Given the increasing incidence of prediabetes and type 2 diabetes (T2D) in the adolescent population in India, it is essential to identify the risk factors associated with these conditions. Understanding the risk factors associated with prediabetes and T2D can lead to timely interventions to prevent and potentially avert long-term health complications. Objective: This study aims to use machine learning algorithms to identify the best anthropometric and demographic characteristics associated with prediabetes and diabetes in Indian children and adolescents ages 10–19. Methods: The study utilizes the Comprehensive National Nutrition Survey conducted in 2016–2018 in India. The study sample includes children and adolescents aged 10–19 years. We used nine supervised machine learning algorithms to classify, assess, and identify the best model for ascertaining the risk of diabetes among adolescents in India. Various indices were used to evaluate the classification algorithms, such as the ‘accuracy score’, ‘F1 score’, ‘recall score’, ‘precision score’, and ‘area under the curve’ (i.e., AUC). Results were obtained based on the model with higher precision and accuracy in predicting the risk of diabetes among study subjects. Cutoff points for prediabetes were between 5.7 and 6.4 mmol/l and diabetes greater than 6.4 mmol/l. Results: The study comprised 12,318 children and adolescents (6333 males and 5985 females). The prevalence of diabetes and prediabetes in the study population was 11% (n = 1888), while the prevalence of diabetes alone was 0.6% (n = 233). WHtR was the most crucial feature in predicting prediabetes/diabetes, with an optimum cutoff of 0.62, a sensitivity of 0.93, and an AUC of 0.79. Conclusions: The findings derived from our machine learning analysis underscore the significance of WHtR as a cost-effective and valuable tool for diabetes and prediabetes screening among adolescents in India.

Actions (Repository Staff Only)

Item Control Page