Abstract:
Heart disease is one of the main causes of death worldwide and the most dangerous ailment. Early identification of cardiovascular disease will reduce mortality. The medical establishment has struggled in recent years to accurately anticipate cardiac disease. According to recent data, one person dies every minute from heart disease. Data science is needed to comprehend the vast volumes of new healthcare data. KNN, LR, AdaBoost, XGB, RF, GB, SVM, and DT machine-learning algorithms are used to forecast cardiac disease. Using these algorithms, we could analyze a person's heart disease risk based on dataset attributes. This study used two types of data. The first heart disease dataset had 918 patient records, 11 attributes, and one target. This dataset combines five well-known cardiac datasets. The second dataset on cardiovascular disease included 70000 patient records, 11 characteristics, and a single goal. This research offers a comparison study by investigating the efficacy of numerous machine learning methods. For our first and second datasets, Gradient Boost (GB) was the most accurate, with 91.80% and 74.50%, respectively. Considering the results of the trial, the Gradient Boost (GB) algorithm has the highest level of accuracy, which is 91.80%, compared to other models and studies being done at the time. A realistic web application is also developed.
