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@article{153294,
        author = {G. Chakravarthi and S.MD. Jabeer },
        title = {HEART DISEASE PREDICTION BASED ON MACHINE LEARNING TECHNIQUES},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {8},
        number = {6},
        pages = {334-339},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=153294},
        abstract = {The latest statistics of the World Health Organization anticipated that cardiovascular diseases including Coronary Heart Disease, Heart attack, vascular disease as the biggest pandemic to the world due to which one-third of the world population would die. With the emerging AI trends, applying an optimal machine learning model to target early detection and accurate prediction of heart disease is indispensable to bring down the mortality rates and to treat cardiac patients with the best clinical decision support. This stems from the motivation of this paper. This paper presents a comprehensive survey on heart disease prediction models derived and validated out of popular heart disease datasets like the Cleveland dataset. The main keywords for the search were Heart Disease, Prediction, Coronary disease, Healthcare, Heart
datasets, and Machine Learning. Results: This review explores the shortcomings of various approaches used for the prediction of heart diseases. It outlines the pros and cons of different research methodologies along with the validation parameters of each reviewed publication
},
        keywords = {Heart diseases, Machine learning, Deep learning, Health care, Heart disease dataset},
        month = {},
        }