Dataset

Overview

Property	Value
Source	Kaggle – Heart Disease UCI
Samples	1,025 patients
Features	13 clinical attributes
Target	Binary — `1` = heart disease present, `0` = absent

The dataset has a balanced class distribution (~51% / 49%), which means accuracy is a meaningful metric here and avoids the pitfalls of imbalanced classification.

Features

Feature	Description
`age`	Age in years
`sex`	Sex (1 = male, 0 = female)
`cp`	Chest pain type (0–3)
`trestbps`	Resting blood pressure (mm Hg)
`chol`	Serum cholesterol (mg/dl)
`fbs`	Fasting blood sugar > 120 mg/dl (1 = true)
`restecg`	Resting ECG results (0–2)
`thalach`	Maximum heart rate achieved
`exang`	Exercise-induced angina (1 = yes)
`oldpeak`	ST depression induced by exercise relative to rest
`slope`	Slope of the peak exercise ST segment
`ca`	Number of major vessels colored by fluoroscopy (0–3)
`thal`	Thalassemia type (0 = normal, 1 = fixed defect, 2 = reversible defect)

Reference

Detrano, R., Janosi, A., Steinbrunn, W., Pfisterer, M., Schmid, J., Sandhu, S., Guppy, K., Lee, S., & Froelicher, V. (1989). International application of a new probability algorithm for the diagnosis of coronary artery disease. American Journal of Cardiology, 64(5), 304–310.