monitor_heart Heart Disease Prediction

This tool provides a probability-based prediction using an ML model. It is NOT a medical diagnosis. Consult a doctor for real medical advice.

Model Accuracy

75% accuracy on the test dataset.

How It Works

A custom logistic regression model analyzes 13 medical inputs to calculate the probability of a person with heart disease.

Note

This is for educational purposes only.

General Information

Your age in years. Higher age slightly increases heart disease risk.

40

Men generally have a higher risk of heart disease at younger ages. In the dataset: 1 = Male, 0 = Female.

Medical Measurements

Measured in mmHg while resting. Normal is around 120.

120

Your total cholesterol level. Above 200 is considered high.

200

Resting ECG (electrocardiogram) checks your heart’s electrical activity while resting.
0 = Normal heart rhythm
1 = Possible ST-T abnormality (may indicate reduced blood flow to the heart)
2 = Signs of left ventricular hypertrophy (the heart muscle is enlarged)

Other Indicators

Maximum heart rate during exercise. Typical range: 120–200 bpm.

150

Chest pain triggered by physical activity. 1 = Yes (pain occurs during exercise) 0 = No (no pain during exercise)

Measures how much your ST segment drops after exercise compared to rest. Higher values indicate stronger signs of heart stress. (Typical range is 0.0 to 5.0)

0

Describes the shape of your ECG line during peak exercise:
0 = Downsloping (highest risk)
1 = Flat
2 = Upsloping (lowest risk)

Number of major blood vessels (0–3) that are visible in a special heart scan. More visible vessels (0–1) usually means good blood flow. Higher values (2–3) can indicate blockages.

0

Thal test shows how your heart handles stress:
1 = Fixed Defect (old damage)
2 = Normal
3 = Reversible Defect (blood flow decreases during exercise)

Predicting...