Abstract: Heart disease, alternatively known as cardiovascular disease, encases various conditions that impact the heart and is the primary basis of death worldwide over the span of the past few decades. It associates many risk factors in heart disease and a need of the time to get accurate, reliable, and sensible approaches to make an early diagnosis to achieve prompt management of the disease. Data analysis and machine learning are the most commonly used techniques for processing enormous data in the healthcare domain. Researchers apply several data mining and machine learning techniques to analyze huge complex medical data, helping healthcare professionals to predict heart disease. This research paper presents various attributes like age, gender, chest pain, cholesterol etc which are used to predict heart attack, and the model is trained using 4 machine learning algorithms namely- Logistic Regression, Gaussian Naïve Bayes, Decision tree and Random Forest algorithm. It uses the existing dataset from the UCI Heart Disease Data set of heart disease patients. The dataset comprises 303 instances and 76 attributes. This research paper aims to envision the probability of developing heart attacks in patients. The results portray that the highest accuracy score is achieved with Logistic Regression.

Keywords: Heart Attack Prediction, Cardiac Analytics, Machine Learning, UCI Heart Disease Data Set, Logistic Regression.

| DOI: 10.17148/IJIREEICE.2021.9703