# Comment on Iacobescu et al. Evaluating Binary Classifiers for Cardiovascular Disease Prediction: Enhancing Early Diagnostic Capabilities. J. Cardiovasc. Dev. Dis. 2024, 11, 396

## TL;DR

This paper critiques a study that claimed nearly perfect accuracy in predicting cardiovascular disease, showing the results were due to flawed methods that leaked data.

## Contribution

The paper identifies and explains how data leakage occurred through improper use of SMOTE-ENN and small kNN parameters, offering practical guidelines to prevent such errors.

## Key findings

- Applying SMOTE-ENN before train/test split caused synthetic data to leak into the test set, inflating accuracy to nearly 99%.
- Using k = 2 in kNN with leaked data further amplified the misleading performance metrics.
- Correcting the workflow reduced accuracy to realistic levels (~80%), aligning with standard benchmarks.

## Abstract

Machine learning is increasingly applied to cardiovascular disease prediction yet reported performance metrics often appear implausibly high due to methodological errors. Recent work has reported nearly perfect predictive accuracy (≈99%) using a k-Nearest Neighbors (kNN) model on CDC heart-disease data. Such performance greatly exceeds typical BRFSS-based benchmarks and strongly indicates data leakage. In this commentary, we replicate and re-analyze the original workflow, showing that the authors applied the SMOTE-ENN resampling method prior to the train/test split, thereby allowing synthetic data generated from the full dataset to contaminate the test set. Combined with an excessively small neighborhood parameter (k = 2), this produced misleadingly high accuracy. It is noted that (1) with SMOTE-ENN performed globally, synthetic samples appear nearly identical to test points, leading to near-perfect classification, and (2) this kNN choice is unusually small for a dataset of this scale and further amplifies leakage bias. Correcting the workflow by restricting oversampling to the training data or using undersampling restores realistic results, reducing predictive accuracy to approximately 80%, confirming the inflation caused by pre-split resampling and aligning with literature norms. This case underscores the critical importance of rigorous validation, transparent reporting, and leakage-free pipelines in medical AI. We outline practical guidelines for avoiding such pitfalls and ensuring reproducible, realistic, and clinically reliable machine-learning studies.