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@article{169408,
        author = {Varad Nikam and Shivangi Pandey and Vipul Ojha and Ayush verma and Shruti Narad and Shanu Khare},
        title = {Empowering Heart Health: A Machine Learning Solution with Telemedicine Integration for Early Detection and Treatment of Cardiovascular Conditions},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {6},
        pages = {984-988},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=169408},
        abstract = {In this paper we have discussed over  the development of a comprehensive machine learning-based prediction model for the diagnosis and management of cardiac disease. We use fifteen factors, such as clinical signs, medical history, and demographics. Following feature engineering and data pretreatment, we examine many strategies to determine which works best. Accuracy is further improved via ensemble approaches. With Apollo 24*7 telemedicine incorporated into our paradigm, real-time consultation is possible. Compliance is ensured with a focus on security and privacy. Our platform seeks to enhance the results and accessibility of healthcare for people who are at risk of heart disease.},
        keywords = {SVM, KNN, EDA, Random Forest, machine learning, and dataset.},
        month = {November},
        }