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@article{177117,
        author = {Suyog Dilip Late and Prof. Nilesh Pardeshi},
        title = {Heart disease detection using AI and ML},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {191-194},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=177117},
        abstract = {Heart disease is one of the leading causes of mortality worldwide. Early detection and accurate diagnosis can significantly reduce the risk of fatal outcomes. This research presents an AI and ML-based predictive model that analyzes medical data to classify patients based on their likelihood of having heart disease. The model utilizes machine learning techniques, including Decision Trees, Random Forest, Support Vector Machine (SVM), and Deep Learning models, to provide an efficient and accurate diagnosis.
The dataset used in this study includes patient health parameters such as age, blood pressure, cholesterol levels, heart rate, and other vital indicators. Feature engineering techniques have been applied to refine the dataset, and hyperparameter tuning has been used to optimize the models. Experimental results indicate that the AI-driven model achieves an accuracy of over 90%, making it a reliable tool for heart disease prediction. This research demonstrates the potential of machine learning in healthcare, aiding medical professionals in early diagnosis and treatment planning.},
        keywords = {Heart Disease Prediction, Machine Learning, Artificial Intelligence, SVM, Deep Learning, Healthcare Analytics.},
        month = {April},
        }