Comparative Analysis of AI Models for Accurate Early Diabetes Detection

Diabetes is a common long-term disease with significant health implications, necessitating early and accurate diagnosis to manage and mitigate its impact. The use of deep learning (DL) and machine learning (ML) algorithms is investigated in this study to predict diabetes based on patient-specific factors, including medical history and lifestyle habits. By leveraging various algorithms, the research evaluates and compares their prediction accuracy to identify the most effective model. The findings aim to improve the early detection of diabetes, providing insights into the performance and feasibility of AI-driven diagnostic approaches in healthcare. In the experimental study we have compared machine learning and deep learning algorithms with the same dataset and found that convolutional neural network outperformed other algorithms.