Early Enhancing Diabetes Diagnosis using Machine Learning, Deep Learning Models, and Clinical Data Analysis

Abstract

Early diagnosis of diabetes is essential for timely intervention and improved patient outcomes. This study proposes an enhanced diagnostic framework that integrates machine learning (ML), deep learning (DL), and clinical data analysis. Using a real-world dataset containing clinical features such as glucose level, BMI, age, and blood pressure, we evaluate the performance of ML models (e.g., decision trees, SVM, random forests) and DL models (e.g., deep neural networks). Data preprocessing and feature selection techniques are applied to improve accuracy and reduce noise. Experimental results show that deep learning models, especially when combined with clinical insights, outperform traditional approaches in early diabetes prediction. This research highlights the potential of AI-based systems to support accurate and early clinical decision-making in diabetes care.