Heart-related diseases or Cardiovascular- Diseases (CVDs) are the main reason for a huge number of death in the world over the last few decades and has emerged as the most life-threatening disease, not only in India but in the whole world. The main objective is to predict the occurrence of heart disease for early automatic diagnosis of the disease within result in a short time. The proposed methodology is also critical in a healthcare organization with experts that have no more knowledge and skill. It uses different medical attributes such as blood sugar and heart rate, age, sex are some of the attributes are included to identify if the person has heart disease or not. Dataset used is a Heart dataset. The dataset is available UCI Machine Learning Repository. The Algorithms used are Random Forest, Support Vector Machines, Naive-Byes, Random Forest, and KNN. The Programming language used is Python, Framework Flask and database is sqlite3 The project involved analysis of the heart disease patient dataset with proper data processing. Then, different models were trained and predictions are made with different algorithms KNN, Decision Tree, Random Forest, SVM, etc.
age (Age in years)
sex : (1 = male, 0 = female)
cp (Chest Pain Type): [ 0: asymptomatic, 1: atypical angina, 2: non-anginal pain, 3: typical angina]
trestbps (Resting Blood Pressure in mm/hg )
chol (Serum Cholesterol in mg/dl)
fps (Fasting Blood Sugar > 120 mg/dl): [0 = no, 1 = yes]
restecg (Resting ECG): [0: showing probable or definite left ventricular hypertrophy by Estes’ criteria, 1: normal, 2: having ST-T wave abnormality]
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): [0: downsloping; 1: flat; 2: upsloping]
ca [number of major vessels (0–3)
thal : [1 = normal, 2 = fixed defect, 3 = reversible defect]
target: [0 = disease, 1 = no disease]
