Heart Diseases Prediction

https://happyheartscapstonemiuul.streamlit.app/ (live project)

This dataset is from 1988 and includes four databases from Cleveland, Hungary, Switzerland, and Long Beach. The dataset comprises 1024 observations and 14 variables, with the “target” variable indicating the presence of heart disease. It is an integer value where 0 = no disease and 1 = disease present.

Our project is a simple Streamlit web application based on users predicting the probability of heart disease based on their characteristics. The prediction is made using a machine learning model trained on heart disease data.

As a result, after the user selects all the features and clicks the “Predict” button, the application will display the predicted outcome and indicate whether the prediction is positive or negative for heart disease.

Variables:

age: Age sex: Gender (0: Female - 1: Male) cp: Chest Pain Types (0: Typical angina - 1: Atypical angina - 2: Non-anginal pain - 3: Asymptomatic) trestbps: Resting Blood Pressure chol: Cholesterol (mg/dl) fbs: Fasting Blood Sugar (0: < 120 mg/dl - 1: > 120 mg/dl) restecg: Resting Electrocardiographic Results (0: Normal - 1: ST-T wave abnormality - 2: Probable or definite left ventricular hypertrophy) thalach: Maximum Heart Rate Achieved exang: Exercise Induced Angina (0: No - 1: Yes) oldpeak: ST Depression Induced by Exercise Relative to Rest slope: The Slope of the Peak Exercise ST Segment (0: Upsloping - 1: Flat - 2: Downsloping) ca: Number of Major Vessels (0-3) thal: Thalassemia, Blood Disorder (0: Normal - 1: Fixed defect - 2: Reversible defect) target: Presence of Heart Disease (0: No - 1: Yes)