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@article{152355,
        author = {Sunita Dhotre and Mayank Ramani and Priyam Maheshwari  and Subhanshu Tripathi},
        title = {Cardiac Care: Heart Disease Prediction Using Machine Learning Techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {8},
        number = {3},
        pages = {238-243},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=152355},
        abstract = {Currently, cardiovascular disease has a significant impact on the death rate. Heart disease is the biggest cause of death all over the world. The procedure of predicting a heart attack can be quite difficult. Heart disease is one of the most difficult diseases, and it has affected many people around the world. There is an abundance of patient data that can be utilized to train a model to predict cardiac disease. Researchers proposed a model that predicts a patient's heart health based on their medical state using various data mining and machine learning techniques. The model is trained on the Cleveland database, which is available on the UCI data repository. It features 303 patient health data records, each of which has 14 different qualities that affect the human heart. The goal of this study is to use supervised learning algorithms like K-nearest and Decision Tree to design a model that can predict the likelihood of patients developing heart disease.},
        keywords = {Cardiovascular disease prediction , machine learning, UCI Dataset. K-Nearest, Decision Tree.},
        month = {},
        }