AI Can Spot Heart Disease, But a New Survey Warns Its Dazzling Accuracy May Be Too Good to Be True

Cardiovascular disease kills roughly 17.9 million people every year, accounting for about 32 percent of all deaths worldwide, and the World Health Organization projects that annual deaths will climb from 20.5 million in 2025 to 35.6 million by 2050—a staggering 90 percent increase over just 25 years. Against that backdrop, a new open-access survey in Discover Informatics has compiled and critically appraised 57 peer-reviewed studies, published mostly between 2020 and 2025, that apply machine learning, deep learning, and explainable artificial intelligence to the detection of heart disease. The review, authored by Md. Siam Ansary of Ahsanullah University of Science and Technology in Dhaka, is as much a warning as it is a celebration: the algorithms are getting astonishingly good, but the evidence behind their headline numbers is often far weaker than the figures suggest.

The technical landscape the survey maps is broad. On structured clinical data—the kind of tabular records found in electronic health records—classical machine learning still dominates. Logistic regression, the same mathematical backbone as the decades-old Framingham Risk Score, achieves a respectable 80 to 86 percent accuracy on the widely used UCI Cleveland dataset of 303 patient records. Support vector machines, which find optimal separating hyperplanes in kernel-transformed feature spaces, reach 80 to 92 percent depending on preprocessing and tuning. But the consistent stars are ensemble methods. Random Forest, which averages many decision trees trained on bootstrapped data subsets, reports 88 to 94 percent accuracy with ROC-AUC values of 0.92 to 0.95, while XGBoost—which builds shallow trees sequentially, each correcting the residual errors of the last—achieves around 93 percent accuracy with an AUC of 0.94 on combined UCI data. LightGBM and CatBoost land within one to two percentage points of XGBoost at lower computational cost.

Deep learning, by contrast, has transformed the analysis of physiological signals and medical images. One-dimensional convolutional neural networks operate directly on raw electrocardiogram waveforms, learning hierarchical, translation-invariant features without manual engineering, and have reported over 99 percent accuracy in five-class arrhythmia classification on the MIT-BIH benchmark. Hybrid CNN-LSTM models, which combine local waveform detection with memory of long-range temporal dependencies, reach 97.8 percent on eight-class arrhythmia detection. The most striking recent development is the arrival of transformers. HeartBEiT, a vision-based transformer pre-trained on 8.5 million ECGs using masked image modelling, outperforms ResNet-152 and EfficientNet-B4 at every training-data fraction, showing a 28.4 percent improvement when only 1 percent of labelled data is available. Another architecture, HCTG-Net, fuses a residual CNN branch for local patterns with a transformer branch for global context through a learnable gated mechanism, achieving 0.9946 accuracy on MIT-BIH.

The survey also documents the rise of explainable artificial intelligence, which has become non-negotiable for clinical adoption. SHAP, grounded in cooperative game theory, assigns each input feature a Shapley value quantifying its marginal contribution to a prediction, and across multiple studies it consistently identifies age, maximum heart rate, chest pain type, and ST depression as the most influential predictors—features that map neatly onto established cardiological knowledge. LIME builds simple local surrogate models around individual predictions, while Grad-CAM highlights the image or waveform regions driving a CNN’s decision. Regulatory frameworks, including the EU AI Act and FDA guidance on software as a medical device, increasingly demand exactly this kind of transparency, and studies report that cardiologists are more willing to accept AI support when explanations align with their clinical reasoning.

Then comes the sobering part. The survey finds that the studies reporting the highest accuracy figures—often above 99 percent—are almost exclusively evaluated on small, single-centre benchmark datasets such as UCI Cleveland, which contains just 303 records collected decades ago, or single-database ECG corpora like MIT-BIH. None of the reviewed studies reporting top performance evaluated their models on an independent cohort from a different institution, country, or demographic. The author’s conclusion is blunt: these numbers should be read as a ceiling on benchmark performance, not as evidence of clinical-grade accuracy. A model that scores 99 percent on 303 records from one 1980s-era clinic tells us very little about how it will behave in a modern, demographically diverse hospital.

Worse, several of the eye-popping figures show telltale signs of methodological trouble. The survey highlights one instructive case in which a deep model reported 96 percent training accuracy against only 88 percent test accuracy on the same pooled dataset—an eight-point gap consistent with overfitting. The review identifies three common culprits behind implausibly high results: applying oversampling techniques like SMOTE before rather than after the train-test split, which leaks synthetic near-duplicates of test cases into training; repeatedly tuning hyperparameters against the same held-out fold; and evaluating on small, manually curated test sets. As a practical heuristic, the survey recommends treating any accuracy above roughly 95 percent on a dataset of a few hundred records with scepticism until the leakage-control procedures have been verified.

The survey is equally candid about explainability. While SHAP, LIME, and Grad-CAM produce explanations that look plausible to technically literate readers, not a single reviewed study conducted a prospective, clinician-in-the-loop evaluation measuring whether these explanations actually change diagnostic decisions, calibrate trust appropriately, or improve patient outcomes. Post-hoc explanations, the author notes, do not verify that a model’s internal reasoning aligns with clinical pathophysiology—a plausible-looking explanation can coexist with a model that has learned a spurious shortcut. Explainability, in other words, is a necessary but not sufficient condition for clinical trust.

On the systems side, the review charts a shift from episodic to continuous cardiac monitoring. Wearable ECG patches, smartwatches, and photoplethysmography sensors now enable ambulatory arrhythmia detection, and CNN-integrated electronic stethoscopes built on Raspberry Pi hardware have screened for valvular disease with 99.35 percent benchmark accuracy. Federated learning, which trains models across hospitals without sharing raw patient data, offers a privacy-preserving route to multi-institutional generalisation, with federated models approaching centralised performance within 2 to 5 percent. But the survey flags unresolved problems: performance degrades when data distributions differ across sites, and the differential-privacy noise added to protect patients trades off directly against accuracy. Fairness audits show that bias-mitigation techniques reduce but do not eliminate differential performance across sex, age, and race—and subgroup performance is rarely reported at all.

Looking forward, the author judges multimodal fusion and federated learning to hold the greatest near-term potential, because both build on data infrastructure health systems already generate, while foundation models pre-trained on tens of millions of ECGs and images represent the biggest long-term bet. Causal inference, digital twins, and continual learning remain earlier-stage. The most urgent need, however, is deceptively simple: randomised controlled trials demonstrating that AI-assisted cardiac diagnosis actually reduces mortality, hospitalisation, and cost compared with standard care. With three-quarters of cardiovascular deaths occurring in low- and middle-income countries where specialist access is scarce, the stakes of getting this right could hardly be higher. The algorithms, this survey makes clear, are ready to be tested—the evidence base simply is not yet ready to be trusted.

Subject of Research: Machine learning, deep learning, and explainable AI methods for detecting cardiovascular disease

Article Title: A comprehensive survey of machine and deep learning as well as explainable artificial intelligence approaches for heart disease detection

Article References: Ansary, M. S. (2026). A comprehensive survey of machine and deep learning as well as explainable artificial intelligence approaches for heart disease detection. Discover Informatics, 1(1), Article 17. https://doi.org/10.1007/s44564-026-00019-w

Image Credits: AI Generated

DOI: 10.1007/s44564-026-00019-w

Keywords: heart disease detection, cardiovascular disease, machine learning, deep learning, explainable AI, SHAP, ECG classification, XGBoost, transformers, federated learning, clinical decision support, overfitting