Optimization of diabetes prediction methods based on combinatorial balancing algorithm.

ABSTRACT:

Background

Diabetes, as a significant disease affecting public health, requires early detection for effective management and intervention. However, imbalanced datasets pose a challenge to accurate diabetes prediction. This imbalance often results in models performing poorly in predicting minority classes, affecting overall diagnostic performance.

Objectives

To address this issue, this study employs a combination of Synthetic Minority Over-sampling Technique (SMOTE) and Random Under-Sampling (RUS) for data balancing and uses Optuna for hyperparameter optimization of machine learning models. This approach aims to fill the gap in current research concerning data balancing and model optimization, thereby improving prediction accuracy and computational efficiency.

Methods

SUBMITTER: Shao H 

PROVIDER: S-EPMC11324958 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature