• Gananjay Sandeep Thanekar

Diabetes is a chronic condition that affects millions of people globally, making early and accurate diagnosis crucial for effective management. Traditional diagnostic methods can be time-consuming and prone to human error. However, recent advancements in machine learning (ML) and deep learning (DL) have transformed disease detection, allowing for automated, efficient, and precise diabetes diagnosis. This paper examines various ML and DL models, including decision trees, support vector machines (SVMs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs), for predicting diabetes. It also explores important datasets, feature selection techniques, and performance metrics to assess the effectiveness of these models. The study highlights the benefits and challenges of implementing AI-driven diagnostic tools in healthcare, stressing the need for robust, interpretable, and clinically validated models.

Diabetes Diagnosis, Machine Learning, Deep Learning, Artificial Intelligence, Neural Networks, Support Vector Machines (SVM), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Predictive Modeling, Healthcare AI.

"Enhancing Diabetes Diagnosis with CRU-Net: A Lightweight Multi-Scale Attention Fusion Approach", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 3, page no.g695-g702, March-2025, Available :http://www.jetir.org/papers/JETIR2503684.pdf

"Enhancing Diabetes Diagnosis with CRU-Net: A Lightweight Multi-Scale Attention Fusion Approach", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 3, page no. ppg695-g702, March-2025, Available at : http://www.jetir.org/papers/JETIR2503684.pdf