Integrating convolutional neural networks with ensemble methods for enhanced diabetes diagnosis: a multi-dataset evaluation.

ABSTRACT:

Introduction

Timely and accurate diagnosis of diabetes mellitus remains a pending challenge due to the diversity of patient data and the limitations of traditional screening methods.

Objective

To propose a hybrid prediction framework incorporating Convolutional Neural Networks (CNNs) and Integrated Learning with a soft voting strategy to improve the accuracy, robustness and interpretability of diabetes diagnosis.

Methods

The model was evaluated on two publicly available datasets-the UCI Pima Indians Diabetes dataset (768 samples, 8 features), the same dataset used to describe the Pima Indians (2,000 samples, 8 features) and the Tianchi Medical dataset (5,642 samples, 41 features). After missing-value imputation, z-score standardization, and min-max normalization, CNNs

SUBMITTER: Zhuang K 

PROVIDER: S-EPMC12518227 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature