Protocol for designing and developing a population-based individualized intervention planning assistant for overweight and obesity.

ABSTRACT: Overweight and obesity are multifactorial health problems requiring innovative analytical approaches. We present a protocol to develop a population-based individualized intervention planning assistant (PIIPA) using supervised machine learning, Shapley additive explanations (SHAP), and a long short-term memory (LSTM) model. We describe steps for constructing interpretable machine learning (ML) models, identifying key predictors, and simulating BMI trajectories under interventions. We then detail procedures for implementing a user interface for individualized planning. This protocol is adaptable to other complex public health issues.

SUBMITTER: Ghalandari H 

PROVIDER: S-EPMC12514346 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature