{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,27]],"date-time":"2026-09-27T11:34:05Z","timestamp":1790508845475,"version":"4.1.0"},"reference-count":36,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T00:00:00Z","timestamp":1772496000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T00:00:00Z","timestamp":1772496000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["chemistry-europe.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Analytical Science Advances"],"published-print":{"date-parts":[[2026,6]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Heart disease (HD) is still one of the most common causes of death around the world. Early detection is very important, but it is often hard to do because the symptoms are not specific and the models are not very clear. We propose a two\u2010layer stacked ensemble that combines four base learners\u2014Support Vector Machine, K\u2010Nearest Neighbors, Na\u00efve Bayes,\u00a0and Decision Tree\u2014with Logistic Regression as a meta\u2010learner. This ensemble was trained on a balanced sample from five public HD datasets. The model outperforms standard baselines with 93.69% accuracy, 93% F1\u2010score, and 94.7% AUC. To ensure that predictions are clinically interpretable, we offer global explainability through SHAP summaries, feature importance, and stacked plots, as well as ELi5 permutation\u2013importance plots. Both identify ST slope and chest pain type as the most significant predictors, consistent with clinical understanding. We use LIME, ELi5 text explanations, SHAP force plots, and waterfall plots to illustrate how different features of a person affect their risk. We utilize SHAP interaction values to examine feature interactions and confirm nonlinear relationships using 1D and 2D partial\u2010dependence plots. Finally, we generate counterfactual explanations to demonstrate minor, plausible changes based on domain knowledge that would affect a prediction. This is useful both for auditability and for conversations around patient\u2010centred care. The framework combines global and local explainable artificial intelligence with strong predictive power to improve decision support and accuracy in HD risk assessment.<\/jats:p>","DOI":"10.1002\/ansa.70072","type":"journal-article","created":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T10:08:21Z","timestamp":1772964501000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Stacked Ensemble Model With Explainable AI for Early Detection of Heart Disease"],"prefix":"10.1002","volume":"7","author":[{"given":"Nazmun","family":"Nahar","sequence":"first","affiliation":[{"name":"Institute of Information Technology Noakhali Science and Technology University  Noakhali Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sanjatul Hasan","family":"Siam","sequence":"additional","affiliation":[{"name":"Institute of Information Technology Noakhali Science and Technology University  Noakhali Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joy","family":"Bhowmik","sequence":"additional","affiliation":[{"name":"Institute of Information Technology Noakhali Science and Technology University  Noakhali Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ayesha Nasrin","family":"Ripa","sequence":"additional","affiliation":[{"name":"Institute of Information Technology Noakhali Science and Technology University  Noakhali Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Md. Hasan","family":"Imam","sequence":"additional","affiliation":[{"name":"Institute of Information Technology Noakhali Science and Technology University  Noakhali Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1126-0153","authenticated-orcid":false,"given":"Haw Jiunn","family":"Woo","sequence":"additional","affiliation":[{"name":"Centre for Ionics University of Malaya Department of Physics Faculty of Science Universiti Malaya  Kuala Lumpur Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hamid","family":"Osman","sequence":"additional","affiliation":[{"name":"Department of Radiological Sciences College of Applied Medical Sciences Taif University  Taif Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mayeen Uddin","family":"Khandaker","sequence":"additional","affiliation":[{"name":"Applied Physics and Radiation Technologies Group CCDCU Faculty of Engineering and Technology Sunway University  Bandar Sunway Selangor Malaysia"},{"name":"Faculty of Graduate Studies Daffodil International University Daffodil Smart City, Birulia Savar  Dhaka Bangladesh"},{"name":"Department of Physics College of Science Korea University  Seoul Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3360-3636","authenticated-orcid":false,"given":"Shams Forruque","family":"Ahmed","sequence":"additional","affiliation":[{"name":"School of Mathematical Sciences Sunway University Bandar Sunway Selangor  Darul Ehsan Malaysia"},{"name":"Miyan Research Institute International University of Business Agriculture and Technology  Dhaka Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,3,3]]},"reference":[{"key":"e_1_2_9_2_1","unstructured":"\"Strongyloidiasis \"Centers for Disease Control and Prevention (CDC) 2020 https:\/\/www.cdc.gov\/dpdx\/strongyloidiasis\/index.html."},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1161\/CIR.0000000000001123"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0251967"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2017.11.287"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.2147\/VHRM.S163869"},{"key":"e_1_2_9_7_1","doi-asserted-by":"crossref","unstructured":"K.Nguyen J. W. Y.Lim K. P.Lee et al. \u201cHeart Disease Classification Using Novel Heterogeneous Ensemble \u201d in2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI)(IEEE 2021):1\u20134 https:\/\/doi.org\/10.1109\/BHI50953.2021.9508614.","DOI":"10.1109\/BHI50953.2021.9508516"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.imu.2019.100203"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/5581806"},{"key":"e_1_2_9_10_1","doi-asserted-by":"crossref","unstructured":"S.Kanwal J.Rashid M. W.Nisar J.Kim andA.Hussain \u201cAn Effective Classification Algorithm for Heart Disease Prediction With Genetic Algorithm for Feature Selection \u201d in2021 Mohammad Ali Jinnah University International Conference on Computing (MAJICC)(IEEE 2021):1\u20136 https:\/\/doi.org\/10.1109\/MAJICC53674.2021.9682657.","DOI":"10.1109\/MAJICC53071.2021.9526242"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.52866\/ijcsm.2023.02.02.004"},{"key":"e_1_2_9_12_1","first-page":"901","article-title":"Combining Knowledge and Data Driven Insights for Identifying Risk Factors Using Electronic Health Records","volume":"2012","author":"Sun J.","year":"2012","journal-title":"AMIA Annual Symposium Proceedings"},{"key":"e_1_2_9_13_1","doi-asserted-by":"crossref","unstructured":"T.Karay\u0131lanand\u00d6.K\u0131l\u0131\u00e7 \u201cPrediction of Heart Disease Using Neural Network \u201d in2017 International Conference on Computer Science and Engineering (UBMK)(IEEE 2017):719\u2013723 https:\/\/doi.org\/10.1109\/UBMK.2017.8093470.","DOI":"10.1109\/UBMK.2017.8093512"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.48047\/IJARIIT.4.2.203"},{"key":"e_1_2_9_15_1","doi-asserted-by":"crossref","unstructured":"M. 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U.Zaman \u201cHeart Disease Prognosis Using Machine Learning Classification Techniques \u201d in2021 6th International Conference for Convergence in Technology (I2CT)(IEEE 2021):1\u20136 https:\/\/doi.org\/10.1109\/I2CT51068.2021.9417927.","DOI":"10.1109\/I2CT51068.2021.9418181"},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.3390\/pr10040749"},{"key":"e_1_2_9_17_1","doi-asserted-by":"publisher","DOI":"10.11591\/ijict.v10i3.10404"},{"key":"e_1_2_9_18_1","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/1817\/1\/012009"},{"key":"e_1_2_9_19_1","doi-asserted-by":"publisher","DOI":"10.1007\/978\u2010981\u201016\u20100544\u20109_54"},{"key":"e_1_2_9_20_1","doi-asserted-by":"crossref","unstructured":"M.Kavitha G.Gnaneswar R.Dinesh Y.Rohith Sai andR.Sai Suraj \u201cHeart Disease Prediction Using Hybrid Machine Learning Model \u201d in2021 6th International Conference on Inventive Computation Technologies (ICICT)(IEEE 2021):1329\u20131333 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