2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 15 , Issue 2 , PP: 151-163, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Yerraginnela Shravani 1 * , Ashesh K. 2
Doi: https://doi.org/10.54216/JISIoT.150211
Cardiopathy is a critical health issue worldwide, accounting for a significant number of fatalities each year. Early and precise prediction of heart-related conditions can substantially reduce mortality rates and improve healthcare outcomes. Although traditional machine learning models have been employed in this domain, their performance often falls short due to challenges like overfitting, limited scalability, and difficulty in capturing intricate, non-linear data patterns. This paper introduces an improved methodology for heart disease prediction by employing advanced machine learning techniques, including deep learning networks, ensemble methods such as CNN and VGG16. Key components of the proposed framework include advanced data pre-processing methods for addressing class imbalance, sophisticated feature engineering driven by domain-specific insights, and comprehensive hyperparameter tuning for enhanced model performance The results of this study reveal significant improvements in predictive accuracy and reliability compared to conventional methods, paving the way for better integration of predictive analytics in cardiovascular healthcare. Future research will focus on integrating dynamic patient data from wearable devices and broadening dataset diversity to enhance the generalizability and fairness of these predictive models.
Heart disease prediction , CNN , ML , VGG16 , DL
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