Using Machine Learning To Predict Type 2 Diabetes With Self-Controllable Lifestyle Risk Factors

Abstract

Globally, the prevalence of diabetes has seen a significant increase, rising from 211 million in 1990 (3.96% of the global population at that time) to 476 million in 2017 (6.31% of the global population in 2017). Extensive research has been conducted to study the causes of diabetes from a data-driven approach, leading to the development of prospective models for predicting future diabetes risks. These studies have highlighted the strong correlation between diabetes and various biomarker factors, such as BMI, age, and certain blood test measures. However, there is a lack of research that focuses on building prospective models to predict future diabetes risks based on lifestyle factors. Therefore, this thesis aims to employ popular machine learning methods to investigate whether it is possible to predict future diabetes using prospective models that incorporate self-controllable lifestyle factors. Our analysis produced remarkable results, with the biomarker model achieving an average validation AUC score of 0.78, while the lifestyle model reached 0.70. Notably, lifestyle features demonstrate a greater predictive capacity for short-term new-onset diabetes when compared to the long-term endpoint. The biomarker model identified visceral fat as the most significant risk factor, whereas income level and employment emerged as the top risk factors in the lifestyle model. This thesis represents an innovative approach to diabetes prediction by leveraging lifestyle factors, providing valuable data-driven insights into the root causes of diabetes. It addresses a critical research gap by highlighting the significant role of lifestyle factors in predicting the future onset of diabetes, particularly within the context of parametric modeling.