Heart disease is a broad term used to refer to diseases and conditions that affect the heart and circulatory system. It is also referred to as cardiovascular disease. It is a significant reason of disability worldwide. Since the heart is one of the most vital organs of the body, its diseases affect other organs and part of the body as well. The main challenge in cardiology is its discovery. There are tools available that can predict heart disease but they are either too expensive or ineffective to calculate the chance of heart disease in humans. Early detection of heart disease can reduce mortality and general complications. However, it is not possible to monitor patients every day accurately in all cases and 24-hour patient consultation by a physician is not available as it requires more wisdom, time and experience. Since we have a large amount of data in today's world, we can use this data to predict whether or not a patient has heart disease based on different criteria and different machine learning algorithms to analyze the data.
Heart Disease Dataset dates from 1988 and consists of four databases: Cleveland, Hungary, Switzerland, and Long Beach V. It contains 76 attributes, including the predicted attribute, but all published experiments refer to using a subset of 14 of them. The "target" field refers to the presence of heart disease in the patient. It is integer valued 0 = no disease and 1 = disease. The dataset consists of 1025 rows and 14 columns, and the data types for all features are int64/float64. (Sourced from: www.kaggle.com/datasets/johnsmith88/heart-disease-dataset).