Analyzing classification and feature selection strategies for diabetes prediction across diverse diabetes datasets.

ABSTRACT:

Introduction

In the evolving landscape of healthcare and medicine, the merging of extensive medical datasets with the powerful capabilities of machine learning (ML) models presents a significant opportunity for transforming diagnostics, treatments, and patient care.

Methods

This research paper delves into the realm of data-driven healthcare, placing a special focus on identifying the most effective ML models for diabetes prediction and uncovering the critical features that aid in this prediction. The prediction performance is analyzed using a variety of ML models, such as Random Forest (RF), XG Boost (XGB), Linear Regression (LR), Gradient Boosting (GB), and Support VectorMachine (SVM), across numerousmedical datasets. The study of feature importance is conducted using metho

SUBMITTER: Kaliappan J 

PROVIDER: S-EPMC11371799 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature