• Aniket Naik
• Sakshi Joshi
• Pranav Kavishwar
• Harshada Shirondkar

The heart plays a vital role in humans. Diagnosis and prognosis of heart-related diseases require clarity, completeness and accuracy because a small error can cause fatigue or death, there are many cases of heart-related deaths and their number is increasing day by day. Dealing with this problem is an important requirement for a predictable disease awareness program. Machine learning by the Artificial Intelligence (AI) branch, provides reputable support in predicting any type of event that takes training at natural events. In this paper, we calculate the accuracy of the cardiovascular predictor learning algorithms, because these algorithms are the nearest neighbour, tree decision, line deviation and support vector machine (SVM) using UCI's final training and testing database. Python programming Anaconda (jupytor) notebook is a great tool, with a wide range of library, title file, performance and precision. Heart disease is one of the most common diseases. The disease is now more common nowadays using a variety of traits that can be closely related to this heart disease to better determine the best way to predict and use algorithms to predict.

Heart Disease Prediction, Machine learning, Logistic regression, Naïve Bayes, Support Vector Machine, Random Forest, Decision tree, Neural Network, Jupytor Notebook.

"Hriday Jatan - A Web Application To Predict Heart Diseases & Suggest Remedial Measures", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 5, page no.e891-e894, May-2021, Available :http://www.jetir.org/papers/JETIR2105649.pdf

"Hriday Jatan - A Web Application To Predict Heart Diseases & Suggest Remedial Measures", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 5, page no. ppe891-e894, May-2021, Available at : http://www.jetir.org/papers/JETIR2105649.pdf