A Heart Disease Prediction Model using Logistic Regression

Abstract

The early prognosis of cardiovascular diseases can aid in making decisions to lifestyle changes in high risk patients and in turn reduce their complications. Research has attempted to pinpoint the most influential factors of heart disease as well as accurately predict the overall risk using homogenous data mining techniques. Recent research has delved into amalgamating these techniques using approaches such as hybrid data mining algorithms. This paper proposes a rule based model to compare the accuracies of applying rules to the individual results of logistic regression on the Cleveland Heart Disease Database in order to present an accurate model of predicting heart disease.

Cardiovascular Disease Prediction Using Machine Learning

“…High blood cholesterol, triglyceride levels, high blood pressure, diabetes and pre-diabetes, overweight and obesity, smoking, lack of physical activity, unhealthy diet, and tension are all linked to high blood cholesterol, triglyceride levels, high blood pressure, diabetes and pre-diabetes, overweight and obesity, smoking, lack of physical activity, unhealthy diet, and tension. Heart disease is currently the biggest cause of death worldwide, according to a World Health Organization report [10] [11], which states that 12 million people die each year as a result of heart disease. Cardiovascular diseases account for half of all deaths in the United States and other industrialised countries.…”

“…Diabetes, obesity, poor diet, physical inactivity, and heavy alcohol use are all conditions and lifestyle behaviours that increase a person's chance of getting heart disease [7] [13]. One of the major organs responsible for the functioning of blood in our human body and for all parts of the body is the heart; heart attack is one of the most common diseases in India; if the heart stops working, the entire blood circulation system in our body stops working, which can lead to death and serious health problems.…”