Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{166870,
        author = {Seetha Lakshmi and Ms.Pavithra and Mrs.Agnes and Ms.Varshini},
        title = {ENHANCING HEART DISEASE DIAGNOSIS USING DEEP LEARNING AND ML ALGORITHMS},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {2},
        pages = {2658-2664},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=166870},
        abstract = {One of the primary causes of mortality worldwide is heart disease, which emphasizes the importance of early and precise diagnostic techniques for better patient outcomes. The present research investigates the use of different deep learning and machine learning algorithms for the prediction of heart disease. Deep learning models, such as convolutional neural networks like ResNet and VGG19, are compared with traditional machine learning methods like the XGBoost support vector machine (SVM), random forest (RF), and linear regression. Using cross-validation approaches, each algorithm is painstakingly trained and refined to maximize performance parameters like precision, recall, precision, and F1-score. In order to improve prediction capabilities, ensemble approaches are investigated as a means of utilizing the advantages of numerous models. The findings show that deep learning models—in particular, CNNs—perform better than conventional machine learning algorithms at identifying intricate relationships in the data, leading to increased sensitivity and accuracy in the prediction of heart disease. The research yields valuable insights that enhance diagnostic approaches in cardiovascular health. These insights provide healthcare practitioners with strong instruments to perform early risk assessment and customize patient care plans. The results highlight how machine learning and deep learning approaches have the potential to transform cardiovascular healthcare by giving physicians dependable decision-support tools. In order to guarantee generalizability and usability in clinical settings, the next research topics include validation across a variety of patient populations and additional investigation of interpretability techniques for deep learning models.},
        keywords = {VGG19, Deep Learning, Convolutional Neural Network (CNN), Medical Imaging, Image Preprocessing.},
        month = {September},
        }