Heart Disease Prediction Using Artificial Intelligence Ensemble Network

Abstract

Heart disease has climbed its way to the top of the list of the primary causes of death all over the world. In the past, individuals also referred to heart disease as cardiovascular disease when talking about it. Heart disease and stroke are the primary causes of death in India, together accounting for one death in every four that occur there. The use of machine learning to the process of forming judgements and creating predictions based on the large quantities of data created by the healthcare business is highly valuable. This is because machine learning can analyse patterns in the data to make more accurate predictions. According to the information that was provided by the WHO, cardiovascular disease is the primary cause of around 24 percent of deaths in India that are attributed to non-communicable illnesses. These deaths are mostly caused by coronary artery disease (CVD). In addition, coronary heart disease is the main cause of death in industrialised nations like the United States of America and other rich countries. Around 17 million people each year lose their lives to cardiovascular disease, making it the leading cause of death on a global scale; the incidence of cardiovascular disease mortality is greatest in Asia. The Cleveland heart disease dataset's primary objective was to provide information that could be used to conduct an analysis of the system. The implementation of the prediction model takes use of a broad range of feature integrations, in addition to numerous techniques of categorization that are already widely known to the general public..