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@article{148019,
        author = {Shaswat Babhulgaonkar and Jayesh Suryavanshi and Pritam Bendkule and Lalit Patil},
        title = {Prediction and Diagnosis of Cardiovascular Diseases using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {5},
        number = {12},
        pages = {45-49},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=148019},
        abstract = {Diagnosis and Prediction of cardiovascular diseases has often become a challenge faced by doctors and hospitals in India as well as abroad. Despite major transformations in lifestyles of people and advancements in medical domain; heart attacks still hold a major share in the global death rate. The ambiguity in diagnosis of most heart diseases lies in the intricate grouping of clinical and pathological data which may introduce misinterpretation of data among clinical experts, doctors and researchers. Ultimately, the problem lies within making decisions concerned with predicting and later diagnosing the heart diseases. These decisions can have a drastic effect on life of a person. The proposed approach to use machine learning for prediction as well as diagnostic purposes can play a very important role in this area. Various Machine Learning techniques can be used for classifying healthy people from the ones suffering from heart diseases. This work intends to present a comprehensive review of prediction of Cardiac diseases by using Machine Learning based approach.},
        keywords = {Cardiovascular diseases, Data Mining, Machine Learning, Neural Networks, SVM},
        month = {},
        }