Cardiovascular Disease prediction Using Data Mining

Abstract

The word "cardiovascular disease" refers to any illness that affects the heart or blood vessels. It's normally linked to fatty deposits in the arteries (atherosclerosis) and an elevated chance of blood clots. When this happens, a blood clot will occur on the plaque, obstructing blood flow. Heart disorder is exacerbated by elevated blood pressure, high cholesterol, and smoke. Diabetes, as well as a variety of other medical problems and lifestyle habits, may increase one's risk of heart failure. We have collected around 1200 dataset. The ever-increasing incidence of Cardiovascular Diseases (CVD) is a big problem for Bangladesh's health sector. The focus of this thesis was to find out how widespread CVD is in Bangladesh, as well as the socio - demographic and way of living factors that influence it. Our study is aimed on the estimation of cardiovascular disease. We developed few features to predict cardiovascular diseases patient's symptoms. We gathered our data from the heart foundation hospital, the peoples hospital, and the al insaf hospital with the assistance of certain doctors and coworkers. We add our algorithm to the most appropriate ten features to forecast the disease. Choose KNN, Logistic regression, Support Vector Machine, Decision Tree, Random Forest, and XGBoost algorithms to really get the best possible results for cardiovascular disease prediction. In the future, we intend to create a model based on this research paper to professionalism, researchers in this field, and random people in determining their heart condition in an efficient manner.