ENHANCING DIABETES DIAGNOSIS USING ADVANCED MACHINE LEARNING AND DEEP LEARNING TECHNIQUES

TLDR

This paper explores various ML and DL models, such as decision trees, support vector machines, convolutional neural networks, and recurrent neural networks, for diabetes prediction, and examines key datasets, feature selection methods, and performance metrics to evaluate model effectiveness.

Abstract

Diabetes is a chronic disease that affects millions worldwide, necessitating early and accurate diagnosis for effective management. Traditional diagnostic methods are often time-consuming and prone to human errors. Recent advancements in machine learning (ML) and deep learning (DL) have revolutionized disease detection by enabling automated, efficient, and precise diabetes diagnosis. This paper explores various ML and DL models, such as decision trees, support vector machines (SVMs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs), for diabetes prediction. It also examines key datasets, feature selection methods, and performance metrics to evaluate model effectiveness. The study highlights the advantages and challenges of using AI-driven diagnostic tools in healthcare, emphasizing the need for robust, interpretable, and clinically validated models.