• P Shikhar

Heart attack, also known as cardiac arrest, has been the leading cause of death worldwide in recent decades. Many risk factors are linked to heart illness, and there is a pressing need for accurate and effective methods to make an early diagnosis and treat the disease. Various medical data mining and machine learning methods are used to gain helpful knowledge about heart disease prediction. However, the precision of the desired results is insufficient. This paper conducted a comparative analysis of different models for classifying the Heart Disease dataset to classify and predict heart attack cases with limited attributes correctly. The novelty of our work is associated with the use of ensemble algorithms over just supervised algorithms. With the total of 10 used so far, Random Forest (RF) gives a maximum of 96.5% for the stated cardiac arrest problem scenarios.

Heart Attack, Cardiac Arrest, Machine Learning, Classification, Classifier models assessment

"Cardiac Attack Prediction Using Data Mining", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.0, Issue 0, page no.0, 0, Available :http://www.jetir.org/papers/JETIR2110152.pdf

"Cardiac Attack Prediction Using Data Mining", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.0, Issue 0, page no. pp0, 0, Available at : http://www.jetir.org/papers/JETIR2110152.pdf