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@article{182859,
        author = {Gananjay Sandeep Thanekar},
        title = {Revolutionizing Diabetes Diagnosis: The Power of Machine Learning and Deep Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {2},
        pages = {3407-3410},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=182859},
        abstract = {Diabetes is a chronic condition affecting millions worldwide, making early and accurate diagnosis essential for effective management. Traditional diagnostic methods are often time-consuming and susceptible to human error. However, advancements in machine learning (ML) and deep learning (DL) have revolutionized disease detection, enabling automated, efficient, and precise diabetes diagnosis. This paper explores various ML and DL models, including decision trees, support vector machines (SVMs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs), for diabetes prediction. It examines key datasets, feature selection techniques, and performance metrics to evaluate model effectiveness. Additionally, the study discusses the benefits and challenges of AI-driven diagnostic tools, emphasizing the need for robust, interpretable, and clinically validated models for real-world healthcare applications.},
        keywords = {Diabetes Diagnosis, Machine Learning, Deep Learning, Artificial Intelligence, Support Vector Machines (SVM), Convolutional Neural Networks (CNN)},
        month = {July},
        }