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 worldwide. Four out of 5CVD deaths are due to heart attacks and strokes, and one-third of these deaths occur prematurely in people under 70 years of age. Heart failure is a common event caused by CVDs and this dataset contains 11 features that can be used to predict a possible heart disease.
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.
Heart disease is a major cause of death worldwide. Early detection of heart attack risk can help in preventing life-threatening complications. Machine learning algorithms can be used to predict the risk of heart attack by analyzing a large dataset of patient records. The main objective of this project is to develop a machine learning model that can accurately predict the likelihood of a heart attack for a given patient based on various risk factors such as age, sex, chest pain, blood pressure, cholesterol levels, blood sugar level, and other medical conditions.
DATASET USED : Kaggle heart-failure-prediction
SOURCE : UCI Machine Learning Repository
Dataset attribute information
age: age in years
sex: sex (1 = male; 0 = female)
cp: chest pain type
-- Value 1: typical angina
-- Value 2: atypical angina
-- Value 3: non-anginal pain
-- Value 4: asymptomatic
trestbps: resting blood pressure (in mm Hg on admission to the hospital)
chol: serum cholestoral in mg/dl
fbs: (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false)
restecg: resting electrocardiographic results
-- Value 0: normal
-- Value 1: having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV)
-- Value 2: showing probable or definite left ventricular hypertrophy by Estes' criteria
thalach: maximum heart rate achieved
exang: exercise induced angina (1 = yes; 0 = no)
oldpeak: ST depression induced by exercise relative to rest
slope: the slope of the peak exercise ST segment
-- Value 1: upsloping
-- Value 2: flat
-- Value 3: downsloping
ca: number of major vessels (0-3) colored by flourosopy
thal: 3 = normal; 6 = fixed defect; 7 = reversable defect
num: diagnosis of heart disease (angiographic disease status)
-- Value 0: < 50% diameter narrowing
-- Value 1: > 50% diameter narrowing
The project's outcome will be a machine learning model that can accurately predict the likelihood of a heart attack for a given patient based on their risk factors, which can assist healthcare professionals in providing early intervention and preventive measures.