Cardiovascular disease

Cardiovascular disease is the leading cause of death for both men and women around the world. Cardiovascular disease (CVD) is a class of diseases that involve the heart or blood vessels. CVD includes coronary artery diseases (CAD) such as angina and heart attack. Other CVDs include hypertensive heart disease, heart failure, carditis, abnormal heart rhythms, rheumatic heart disease, cardiomyopathy, congenital heart disease, valvular heart disease, thromboembolic disease, peripheral artery disease, aortic aneurysms, and venous thrombosis. Together CVD resulted in 17.9 million deaths (32.1%). Coronary artery disease and stroke account for 80% of CVD deaths in males and 75% of CVD deaths in females. Most cardiovascular disease affects older adults. The average age of death from coronary artery disease in the developed world is around 80 while it is around 68 in the developing world. Diagnosis of disease typically occurs seven to ten years earlier in men as compared to women. Heart failure is a common event caused by CVDs and this dataset contains 12 features that can be used to predict mortality by heart failure. Most cardiovascular diseases can be prevented by medical treatment and excluding risk factors such as unhealthy diet, tobacco use, physical inactivity and harmful use of alcohol. Survivors of a heart attack or stroke are at high risk of recurrences and at high risk of dying from them. The risk of a recurrence or death can be substantially lowered with a combination of drugs – statins to lower cholesterol, drugs to lower blood pressure, and aspirin. People with cardiovascular disease or who are at high cardiovascular risk need early detection and a machine learning can assist in it.

Data Set

Cardiovascular diseases (CVDs) are the number 1 cause of death globally, taking an estimated 17.9 million lives each year, which accounts for 31% of all deaths worlwide. Heart failure is a common event caused by CVDs and this dataset contains 12 features that can be used to predict mortality by heart failure. Most cardiovascular diseases can be prevented by addressing behavioural risk factors such as tobacco use, unhealthy diet and obesity, physical inactivity and harmful use of alcohol using population-wide strategies. People with cardiovascular disease or who are at high cardiovascular risk (due to the presence of one or more risk factors such as hypertension, diabetes, hyperlipidaemia or already established disease) need early detection and management wherein a machine learning model can be of great help.

Dataset from Davide Chicco, Giuseppe Jurman: “Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone. BMC Medical Informatics and Decision Making 20, 16 (2020)