Heart Disease Prediction using Artificial Intelligence and Machine Learning

Abstract

Heart-related medical conditions, often known as cardiovascular diseases (CVDs), have emerged as the main cause for death worldwide over the past several decades and are now recognized as the most serious medical conditions in both India and the rest in the entire world. This study focuses on the development of a machine learning-based artificial intelligence system for the identification of heart disease. Diagnose, risk stratification, and management are basically a few of the critical thinking-intensive aspects of medical treatment that have been mechanized because of the development of artificial intelligence (AI) and data science, which has reduced the strain on doctors and decreased the possibility of human error. Using the primary clinical outcomes of each CHD (Congenital heart defects) and the right computational algorithms, risk stratification as well as the prediction of treatment results are feasible. Cardiovascular diseases (CVDs) have a significant economic burden for healthcare systems all over the world and are associated with high morbidity and mortality. In the United States, adults had a prevalence of 7.2% for coronary artery disease, which is one disease category included under the CVDs umbrella. In the years 2016–2017, this disease entity damaged the United States economy 360 billion US dollars. The domain of cardiovascular medicine has expanded in new directions because of the development of artificial intelligence (AI) as well as machine learning over the past two decades. It will be highly beneficial to the treatment of this disease if we are able to predict cardiac arrest in the early stages. A quick and effective detection method must be developed in order to decrease the enormous number of deaths caused by cardiovascular diseases. In order to increase the model's ability for predicting heart disease in every individual, an effective approach was utilized to control how it can be used. Keywords: Artificial intelligence, Pediatric cardiac surgeries, Heart disease detection system, Cardiovascular diseases, Coronary artery disease, Congenital heart defects