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@article{165388,
        author = {Mansi Singh Parihar},
        title = {HEART DISEASE PREDICTION USING DEEP LEARNING – A Survey},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {11},
        number = {1},
        pages = {1177-1183},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=165388},
        abstract = {In this work, a unique approach to the early and accurate identification of heart disease—a leading cause of mortality worldwide—is discussed. Recognizing the urgent need to address this health concern, the research highlights the utilization of cutting-edge medical technology as the main diagnostic tool, namely Electrocardiogram (ECG) data. In an effort to increase detection accuracy and prognostic capacity for cardiovascular illness, the study looks into a variety of machine learning (ML) and deep learning (DL) models. The suggested technique intends to improve the diagnosis of heart illness by using ECG signals, which offer important information regarding heart function. The paper's creative methodology advances the development of effective and trustworthy instruments for the early detection of cardiovascular disorders. Through the integration of cutting-edge machine learning and deep learning techniques, the research hopes to have a major influence on healthcare and, in the end, save lives by treating patients at risk of heart-related diseases in a timely manner. The potential of technology to lower the worldwide burden of cardiovascular illnesses is highlighted by this work, which marks a significant improvement in medical diagnosis.},
        keywords = {Heart Disease, Cardiovascular Disease, Early Detection, Deep Learning, Machine Learning, Medical Diagnostics, Risk Prediction, Big Data, AI, Feature Extraction, Neural Networks, Predictive Models, and Healthcare Technology.},
        month = {},
        }