{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T16:21:47Z","timestamp":1784132507733,"version":"3.55.0"},"reference-count":73,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2025,6,2]],"date-time":"2025-06-02T00:00:00Z","timestamp":1748822400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Heart attack is a leading cause of mortality, necessitating timely and precise diagnosis to improve patient outcomes. However, timely diagnosis remains a challenge due to the complex and nonlinear relationships between clinical indicators. Machine learning (ML) and deep learning (DL) models have the potential to predict cardiac conditions by identifying complex patterns within data, but their \u201cblack-box\u201d nature restricts interpretability, making it challenging for healthcare professionals to comprehend the reasoning behind predictions. This lack of interpretability limits their clinical trust and adoption. The proposed approach addresses this limitation by integrating predictive modeling with Explainable AI (XAI) to ensure both accuracy and transparency in clinical decision-making. The proposed study enhances heart attack prediction using the University of California, Irvine (UCI) dataset, which includes various heart analysis parameters collected through electrocardiogram (ECG) sensors, blood pressure monitors, and biochemical analyzers. Due to class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied to enhance the representation of the minority class. After preprocessing, various ML algorithms were employed, among which Artificial Neural Networks (ANN) achieved the highest performance with 96.1% accuracy, 95.7% recall, and 95.7% F1-score. To enhance the interpretability of ANN, two XAI techniques, specifically SHapley Additive Explanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME), were utilized. This study incrementally benchmarks SMOTE, ANN, and XAI techniques such as SHAP and LIME on standardized cardiac datasets, emphasizing clinical interpretability and providing a reproducible framework for practical healthcare implementation. These techniques enable healthcare practitioners to understand the model\u2019s decisions, identify key predictive features, and enhance clinical judgment. By bridging the gap between AI-driven performance and practical medical implementation, this work contributes to making heart attack prediction both highly accurate and interpretable, facilitating its adoption in real-world clinical settings.<\/jats:p>","DOI":"10.3390\/a18060333","type":"journal-article","created":{"date-parts":[[2025,6,2]],"date-time":"2025-06-02T08:34:13Z","timestamp":1748853253000},"page":"333","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Enhancing Heart Attack Prediction: Feature Identification from Multiparametric Cardiac Data Using Explainable AI"],"prefix":"10.3390","volume":"18","author":[{"given":"Muhammad","family":"Waqar","sequence":"first","affiliation":[{"name":"Department of Software Engineering, National University of Modern Languages, Islamabad 44800, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1984-3406","authenticated-orcid":false,"given":"Muhammad Bilal","family":"Shahnawaz","sequence":"additional","affiliation":[{"name":"Department of Information Engineering Technology, National Skills University, Islamabad 44310, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sajid","family":"Saleem","sequence":"additional","affiliation":[{"name":"School of Computing, Skyline University College, University City Sharjah, Sharjah 1797, United Arab Emirates"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hassan","family":"Dawood","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, University of Engineering and Technology-Taxila, Taxila 47050, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Usman","family":"Muhammad","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Aalto University, 02150 Espoo, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2653-9541","authenticated-orcid":false,"given":"Hussain","family":"Dawood","sequence":"additional","affiliation":[{"name":"School of Computing, Skyline University College, University City Sharjah, Sharjah 1797, United Arab Emirates"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1161\/CIRCULATIONAHA.120.053216","article-title":"Spending on cardiovascular disease and cardiovascular risk factors in the United States: 1996 to 2016","volume":"144","author":"Birger","year":"2021","journal-title":"Circulation"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3615","DOI":"10.1097\/MS9.0000000000002138","article-title":"Machine learning in heart failure diagnosis, prediction, and prognosis","volume":"86","author":"Saqib","year":"2024","journal-title":"Ann. 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