A machine learning-based diagnostic model for early detection and accurate classification of heart disease in patients.
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 5 CVD 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. We are using a dataset that contains 11 features to train and test our machine learning model.
- Age: The age of the patient
- Sex: The gender of the patient
- Chest Pain Type: Type of chest pain experienced
- Resting Blood Pressure: Resting blood pressure in mm Hg
- Serum Cholesterol: Serum cholesterol in mg/dl
- Fasting Blood Sugar: Fasting blood sugar > 120 mg/dl
- Resting ECG: Resting electrocardiographic results
- Max Heart Rate: Maximum heart rate achieved
- Exercise Induced Angina: Exercise induced angina
- ST Depression: ST depression induced by exercise relative to rest
- Slope: The slope of the peak exercise ST segment
- Number of Major Vessels: Number of major vessels colored by fluoroscopy
- Thalassemia: Thalassemia status
- Clone the repository:
git clone https://github.com/cepdnaclk/e19-co544-Heart-Disease-Prediction-System.git