PYTHON ENSEMBLE LEARNING FOR EARLY CARDIOVASCULAR DISEASE DIAGNOSIS

Abstract

The early detection of heart disease based on symptoms is a major challenge in today's world, particularly in developing countries where access to specialized heart doctors is limited in remote and rural areas. To tackle this issue, researchers have proposed a hybrid decision support system that aids in the early detection of heart disease using clinical parameters of patients. In recent years, there has been a growing emphasis on predicting cardiovascular disease using data-driven techniques and machine learning algorithms. Early detection of cardiovascular disease poses a significant challenge for clinicians, as it is influenced by multiple variables such as blood pressure, cholesterol levels, and pulse rate. Artificial intelligence, particularly machine learning and deep learning models, can play a vital role in early identification and treatment of the disease. Another research paper proposes an ensemble-based approach utilizing six classification algorithms to predict the likelihood of developing cardiovascular disease. The random forest algorithm is employed to extract important features related to cardiovascular disease from a publicly available dataset. This research paper proposes an ensemble-based approach that combines machine learning (ML) and deep learning (DL) models to predict the probability of an individual developing cardiovascular disease. The study utilizes six classification algorithms to achieve this prediction, training the models using a publicly available dataset consisting of causes related to cardiovascular disease. Specifically, the random forest (RF) algorithm is employed to extract essential features relevant to cardiovascular disease.