A Source-Aware Data Audit and Exploratory Analysis Framework for a Hybrid Coronary Artery Disease Dataset

This study presents a reproducible, source-aware data audit and exploratory data analysis framework for a hybrid Coronary Artery Disease (CAD) dataset constructed from four sources: Cleveland, Hungarian, Switzerland, and Long Beach VA. The study focuses on dataset provenance, schema validation, target coding, missing-value analysis, repeated clinical records, invalid-value checks, feature distributions, categorical frequencies, relationships between clinical attributes, and exploratory imputation strategies. The dataset comprises 920 records with 13 clinical predictors and a binary target, exhibiting substantial missingness in key attributes and considerable variation in target prevalence across sources. By systematically examining overall and source-specific data characteristics, this study establishes a transparent data-quality and preprocessing foundation for subsequent machine learning research. The findings highlight the importance of preserving source information and addressing data-quality challenges before predictive modelling, without making claims about predictive performance or clinical readiness.