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@article{169976,
        author = {Sana Muskan Sheikh and R.Sethu madhavi},
        title = {Heart Disease Prediction Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {6},
        pages = {2783-2788},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=169976},
        abstract = {The number of heart disease cases is rising quickly every day, making it crucial and worrisome to anticipate any such illnesses in advance. This diagnosis is a challenging task that requires accuracy and efficiency. The study article primarily focuses on identifying patients who, given a variety of medical characteristics, are more likely to suffer heart disease. Using the patient's medical history, we developed a heart disease prediction algorithm to determine the likelihood of a heart disease diagnosis or not. Utilizing various machine learning methods, including logistic regression and KNN, we were able to predict and categorize the patient with heart disease. A very useful method was employed to control how the model can be applied to increase the precision of a person's heart attack prediction. The suggested model's strength was quite pleasing; it could accurately detect signs of heart illness in a specific person using KNN and Logistic Regression, outperforming other classifiers like Naïve Bayes, among others, with a good degree of accuracy. Thus, by utilizing the provided model to determine the likelihood that the classifier can correctly and precisely diagnose cardiac illness, a sizable amount of pressure has been released. The Given heart disease prediction system lowers costs and improves medical care. We get important information from this experiment that will aid in the prediction of heart disease patients.},
        keywords = {},
        month = {November},
        }