Description

This GitHub project aims to predict heart disease based on various medical metrics such as age, sex, chest pain type, resting blood pressure, serum cholesterol level, fasting blood sugar, resting electrocardiographic results, maximum heart rate, exercise-induced angina, ST depression, slope of the peak exercise ST segment, number of major vessels colored by fluoroscopy, and thalassemia. The project includes a trained model that achieves an accuracy score of 86.79% on the test data.

Object

To Predict Heart disease according to following metrics.

Description

age: Age of the patient in years.

sex: Gender of the patient (typically coded as 0 for female and 1 for male).

cp: Chest pain type (0 to 3):

• 0: Typical angina
• 1: Atypical angina
• 2: Non-anginal pain
• 3: Asymptomatic

trestbps: Resting blood pressure (in mm Hg) measured at the time of the test.

chol: Serum cholesterol level (in mg/dl).

fbs: Fasting blood sugar (1 if > 120 mg/dl, 0 otherwise).

restecg Resting electrocardiographic results (0 to 2):

• 0: Normal
• 1: Having ST-T wave abnormality
• 2: Showing probable or definite left ventricular hypertrophy

thalach Maximum heart rate achieved during exercise (in beats per minute).

exang Exercise induced angina (1 if yes, 0 if no).

oldpeak ST depression induced by exercise relative to rest (measured in mm).

slope Slope of the peak exercise ST segment (0 to 2):

• 0: Upsloping
• 1: Flat
• 2: Downsloping

ca Number of major vessels (0-3) colored by fluoroscopy.

thal Thalassemia (0 to 3):

• 0: Normal
• 1: Fixed defect
• 2: Reversible defect

target Diagnosis of heart disease (1 if presence, 0 if absence).

Train Result:

======================= Accuracy Score: 86.79%

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Classification Report: 0 1 accuracy macro avg weighted avg precision 0.88 0.86 0.87 0.87 0.87 recall 0.82 0.90 0.87 0.86 0.87 f1-score 0.85 0.88 0.87 0.87 0.87 support 97.00 115.00 0.87 212.00 212.00

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Confusion Matrix: [[ 80 17] [ 11 104]]