Machine learning framework for predicting susceptibility to obesity.

ABSTRACT: Obesity, currently the fifth leading cause of death worldwide, has seen a significant increase in prevalence over the past four decades. Timely identification of obesity risk facilitates proactive measures against associated factors. In this paper, we proposed a new machine learning framework for predicting susceptibility to obesity called ObeRisk. The proposed model consists of three main parts, preprocessing stage (PS), feature stage (FS), and obesity risk prediction (OPR). In PS, the used dataset was preprocessed through several processes; filling null values, feature encoding, removing outliers, and normalization. Then, the preprocessed data passed to FS where the most useful features were selected. In this paper, we introduced a new feature selection methodology called entropy-control

SUBMITTER: Shaban WM 

PROVIDER: S-EPMC12508184 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature