Early Heart Disease Prediction Using Machine Learning Algorithm

One of the most common health problems in the world, cardiovascular disease accounts for around 32% of all fatalities yearly. Effective treatment and illness management of cardiac disorders depend on early detection and diagnosis. In spite of medical professionals efforts, Misdiagnosis and misunderstanding of test results by cardiologists and cardiovascular surgeons may occur daily. According to the World Health Organization (WHO), cardiovascular diseases (CVDs) cause 32% of all deaths around the world, which makes them a significant global health concern. As Artificial Intelligence (AI) techniques like as Machine Learning (ML) and Deep Learning (DL) have advanced, they have become essential tools for detecting and predicting CVDs. By carefully comparing a number of strong existing machine learning algorithms, this study seeks to create an ML system for the early prediction of cardiovascular illnesses.

Cardiovascular diseases (CVDs) such as hypertension, heart failure, stroke, and coronary artery disease are now the major causes of early death worldwide, particularly in low and middle-income countries. Early detection of these disorders could lower the number of people who die prematurely. Researchers have proposed many techniques for CVD prediction, such as data mining, machine learning (ML), and the Internet of Things (IoT), for the early detection and monitoring of cardiac patients. Although these techniques are suggested and sometimes used, there is still much worry regarding their efficacy in situations where the error rate is high and accuracy is doubtful. As a result, it is necessary to select a prediction technique that can deliver more accuracy and fewer errors. This paper proposes an effective ensemble method based on the Random Forest (RF) algorithm for improving accuracy by combining multiple feature selection technique.