• Ankit Kumar Meher
• Rahul Tirkey
• Ansh Kumar

Approximately one person dies from heart disease every minute in today's medical world, making prediction difficult. Healthcare data is handled more easily by data science, but automating forecasts can lower risks and notify patients in advance. Using a dataset on heart disease, this study applies various machine learning techniques, including Naive Bayes, Decision Trees, Logistic Regression, K-NN, and Random Forest, to predict and categorize risk levels. At 90.16%, Random Forest had the highest accuracy. Machine learning aids in the extraction of valuable information from data, particularly in the highly complex and fatal prediction of heart disease. To analyse large datasets and improve predictions, a variety of techniques are employed. The ethical and privacy issues surrounding the use of personal data can have an impact on how trustworthy prediction systems are. Even with these difficulties, machine learning algorithms such as Random Forest exhibit potential in precisely forecasting heart disease.

"Heart disease prediction system using machine learning ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 5, page no.f312-f319, May-2024, Available :http://www.jetir.org/papers/JETIR2405534.pdf

"Heart disease prediction system using machine learning ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 5, page no. ppf312-f319, May-2024, Available at : http://www.jetir.org/papers/JETIR2405534.pdf