{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T16:59:00Z","timestamp":1772989140927,"version":"3.50.1"},"reference-count":59,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2025,11,28]],"date-time":"2025-11-28T00:00:00Z","timestamp":1764288000000},"content-version":"vor","delay-in-days":331,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100007446","name":"King Khalid University","doi-asserted-by":"publisher","award":["RGP2\/109\/46"],"award-info":[{"award-number":["RGP2\/109\/46"]}],"id":[{"id":"10.13039\/501100007446","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Applied Computational Intelligence and Soft Computing"],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:p>Cardiovascular diseases encompass a range of conditions affecting the heart and blood vessels. Given their global impact, early detection of these diseases is crucial for saving lives and effectively managing morbidity and mortality. One such effective approach is to leverage deep learning to enhance classification performance in heart disease prediction (HDP). This paper presents two new simplified deep neural network (SDNN) models to assist cardiologists and vascular doctors in diagnosing heart disease that achieve 100% accuracy with feature selection, outperforming complex hybrid models of related works. The proposed models are tested on combined Kaggle datasets containing consistent reports of 918 people, including heart disease and non\u2010heart disease reports. The models are investigated with\/without applying feature selection methods and compared with different machine learning classifiers and recently proposed SDNN models. The experimental results show how the proposed SDNN models outperform other ML\u2010based and DL\u2010based classifiers in terms of accuracy and structure\u2019s complexity. The proposed model, SDNN\u2013HDP1, with dropout layers achieves 94.086% accuracy, while the second proposed model, SDNN\u2013HDP2, without dropout layers achieves 95.69% accuracy without feature selection. The results of SDNN\u2013HDP1 and SDNN\u2013HDP2 reach 100% in terms of accuracy, precision, and recall with feature selection.<\/jats:p>","DOI":"10.1155\/acis\/8709881","type":"journal-article","created":{"date-parts":[[2025,11,28]],"date-time":"2025-11-28T14:51:31Z","timestamp":1764341491000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Simplified Deep Neural Network Models for Cardiovascular Disease Classification"],"prefix":"10.1155","volume":"2025","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8451-4241","authenticated-orcid":false,"given":"Farouk Abduh Kamil","family":"Al-Fahaidy","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7174-6083","authenticated-orcid":false,"given":"Mohamad Yahya H.","family":"Al-Shamri","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5319-2067","authenticated-orcid":false,"given":"Abdullatif","family":"Ghallab","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ahmed Fuad","family":"Aldubai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7912-8629","authenticated-orcid":false,"given":"Belal","family":"Al-Fuhaidi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sadik","family":"Al-Taweel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohamed","family":"Al-Olofy","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abdullah Hameed Ali","family":"Naji","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Moath Faisal Ali","family":"Ahmed","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,11,28]]},"reference":[{"key":"e_1_2_15_1_2","unstructured":"World Health Organization Cardiovascular Disease (CVDs) 2021 https:\/\/www.who.int."},{"key":"e_1_2_15_2_2","doi-asserted-by":"publisher","DOI":"10.1093\/med\/9780198766223.003.0001"},{"key":"e_1_2_15_3_2","doi-asserted-by":"publisher","DOI":"10.1111\/echo.14220"},{"key":"e_1_2_15_4_2","unstructured":"FogorosR. N. How Heart Disease is Diagnosed 2021 https:\/\/www.heartdisease.com."},{"key":"e_1_2_15_5_2","doi-asserted-by":"publisher","DOI":"10.4236\/health.2021.139077"},{"key":"e_1_2_15_6_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13369-020-05105-1"},{"key":"e_1_2_15_7_2","first-page":"3194","article-title":"Deep Learning Approach for Prediction of Heart Disease Using Data Mining Classification Algorithm Deep Belief Network","volume":"4","author":"Karthikeyan T.","year":"2017","journal-title":"International Journal of Advanced Research in Science, Engineering and Technology"},{"key":"e_1_2_15_8_2","article-title":"Heart Attack Prediction Using Deep Learning","volume":"5","author":"Kishore A.","year":"2018","journal-title":"International Research Journal of Engineering and Technology (IRJET)"},{"key":"e_1_2_15_9_2","first-page":"281","article-title":"Heart Disease Prediction Using Data Mining Techniques","volume":"10","author":"Shetgaonkar P.","year":"2021","journal-title":"International Journal of Engineering Research and Technology"},{"key":"e_1_2_15_10_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12911-021-01527-5"},{"key":"e_1_2_15_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2020.3007561"},{"key":"e_1_2_15_12_2","doi-asserted-by":"publisher","DOI":"10.3390\/app142210516"},{"key":"e_1_2_15_13_2","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/3895976"},{"key":"e_1_2_15_14_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12911-020-1023-5"},{"key":"e_1_2_15_15_2","doi-asserted-by":"publisher","DOI":"10.35940\/ijitee.c9009.019320"},{"key":"e_1_2_15_16_2","article-title":"Support Vector Machine: Introduction to Machine Learning Algorithms","volume":"7","author":"Gandhi R.","year":"2018","journal-title":"Data Science"},{"key":"e_1_2_15_17_2","article-title":"Decision Trees in Machine Learning","author":"Gupta P.","year":"2017","journal-title":"Data Science"},{"key":"e_1_2_15_18_2","article-title":"K-Nearest Neighbor","author":"Christopher A.","year":"2021","journal-title":"Start-Up"},{"key":"e_1_2_15_19_2","article-title":"Logistic Regression Detailed Overview","author":"Swaminathan S.","year":"2018","journal-title":"Data Science"},{"key":"e_1_2_15_20_2","article-title":"Naive Bayes Classifier","author":"Gandhi R.","year":"2018","journal-title":"Data Science"},{"key":"e_1_2_15_21_2","article-title":"Understanding Random Forest","author":"Yiu T.","year":"2019","journal-title":"Data Science"},{"key":"e_1_2_15_22_2","article-title":"Beginner\u2019s Guide to Artificial Neural Networks","author":"Dhingra D.","year":"2021","journal-title":"Analytics Vidhya"},{"key":"e_1_2_15_23_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2019.2923707"},{"key":"e_1_2_15_24_2","doi-asserted-by":"publisher","DOI":"10.22266\/ijies2019.0228.24"},{"key":"e_1_2_15_25_2","doi-asserted-by":"publisher","DOI":"10.3390\/pr10040749"},{"key":"e_1_2_15_26_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.105624"},{"key":"e_1_2_15_27_2","doi-asserted-by":"crossref","unstructured":"KavithaM. GnaneswarG. DineshR. SaiY. R. andSurajR. S. Heart Disease Prediction Using Hybrid Machine Learning Model 2021 6th International Conference on Inventive Computation Technologies (ICICT) 2021 Coimbatore India 1329\u20131333 https:\/\/doi.org\/10.1109\/ICICT50816.2021.9358597.","DOI":"10.1109\/ICICT50816.2021.9358597"},{"key":"e_1_2_15_28_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2024.3350996"},{"key":"e_1_2_15_29_2","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics12061474"},{"key":"e_1_2_15_30_2","doi-asserted-by":"crossref","unstructured":"RamprakashP.et al. Heart Disease Prediction Using Deep Neural Network 2020 International Conference on Inventive Computation Technologies (ICICT) 2020 IEEE 666\u2013670.","DOI":"10.1109\/ICICT48043.2020.9112443"},{"key":"e_1_2_15_31_2","doi-asserted-by":"crossref","unstructured":"HussainS.et al. Novel Deep Learning Architecture for Predicting Heart Disease Using CNN 2021 19th OCIT International Conference on Information Technology (OCIT) 2021 IEEE.","DOI":"10.1109\/OCIT53463.2021.00076"},{"key":"e_1_2_15_32_2","doi-asserted-by":"publisher","DOI":"10.1007\/s42979-020-0097-6"},{"key":"e_1_2_15_33_2","doi-asserted-by":"publisher","DOI":"10.3390\/biomedicines10112796"},{"key":"e_1_2_15_34_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2020.06.008"},{"key":"e_1_2_15_35_2","doi-asserted-by":"publisher","DOI":"10.51983\/ajcst-2019.8.2.2141"},{"key":"e_1_2_15_36_2","doi-asserted-by":"publisher","DOI":"10.14569\/ijacsa.2021.0120695"},{"key":"e_1_2_15_37_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12530-025-09664-2"},{"key":"e_1_2_15_38_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13755-025-00361-7"},{"key":"e_1_2_15_39_2","unstructured":"LamirA. A. RazzagzadehS. andRezaeiZ. A Comprehensive Machine Learning Framework for Heart Disease Prediction: Performance Evaluation and Future Perspectives 2025 https:\/\/arxiv.org\/abs\/2505.09969."},{"key":"e_1_2_15_40_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.aej.2024.12.035"},{"key":"e_1_2_15_41_2","doi-asserted-by":"publisher","DOI":"10.2166\/wcc.2024.558"},{"key":"e_1_2_15_42_2","doi-asserted-by":"publisher","DOI":"10.23939\/mmc2025.02.384"},{"key":"e_1_2_15_43_2","unstructured":"Kaggle Heart Disease Dataset 2022 https:\/\/www.kaggle.com\/datasets\/johnsmith88\/heart-disease-dataset."},{"key":"e_1_2_15_44_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2015.06.024"},{"key":"e_1_2_15_45_2","volume-title":"Data Preparation for Machine Learning: Data Cleaning, Feature Selection, and Data Transforms in Python","author":"Brownlee J.","year":"2020"},{"key":"e_1_2_15_46_2","unstructured":"JordanJ. Deep Neural Networks: Preventing Overfitting 2022 https:\/\/www.jeremy.Jordan.me\/deep-neura-networks-preventing-overfitting."},{"key":"e_1_2_15_47_2","doi-asserted-by":"publisher","DOI":"10.1186\/s13640-024-00619-2"},{"key":"e_1_2_15_48_2","volume-title":"Machine Learning Fundamentals: Use Python and Scikit-Learn to Get Up and Running with the Hottest Developments in Machine Learning","author":"Saleh H.","year":"2018"},{"key":"e_1_2_15_49_2","first-page":"13","article-title":"Discrete Sine Transform Based OFDMA System for Wireless Broadband Communications","volume":"6","author":"Al-Fuhaidy F. A. K.","year":"2019","journal-title":"AASCIT Communications"},{"key":"e_1_2_15_50_2","doi-asserted-by":"publisher","DOI":"10.11648\/j.ajece.20190301.11"},{"key":"e_1_2_15_51_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-018-3662-3"},{"key":"e_1_2_15_52_2","article-title":"The Mathematics of Decision Trees, Random Forest, and Feature Importance in Scikit-Learn and Spark","author":"Ronaghan S.","year":"2018","journal-title":"Data Science"},{"key":"e_1_2_15_53_2","doi-asserted-by":"publisher","DOI":"10.1098\/rsta.2015.0202"},{"key":"e_1_2_15_54_2","doi-asserted-by":"publisher","DOI":"10.1155\/2024\/2625922"},{"key":"e_1_2_15_55_2","doi-asserted-by":"crossref","unstructured":"Al-FuhaidiB. L.et al. Anomaly-Based Intrusion Detection System in WSN Using DNN Algorithm 2024 1st International Conference on Emerging Technologies for Dependable Internet of Things (ICETI) 2024 \u200f.","DOI":"10.1109\/ICETI63946.2024.10777266"},{"key":"e_1_2_15_56_2","unstructured":"OpenGenusI. Q. Relu Activation Function 2022 https:\/\/iq.opengenus.org."},{"key":"e_1_2_15_57_2","volume-title":"A Gentle Introduction to Sigmoid Function","author":"Saeed M.","year":"2021"},{"key":"e_1_2_15_58_2","unstructured":"SingleCare Heart Disease Statistics 2022 https:\/\/www.singlecare.com."},{"key":"e_1_2_15_59_2","unstructured":"Scikit-learn Machine Learning in Python 2022 Sklearn.Neighbors.KNeighborsClassifier\u2014scikit-learn 1.0.2 Documentation Sklearn.svm.SVC\u2014Scikit-Learn 1.0.2 Documentation."}],"container-title":["Applied Computational Intelligence and Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/acis\/8709881","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1155\/acis\/8709881","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/acis\/8709881","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T11:48:03Z","timestamp":1772970483000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/acis\/8709881"}},"subtitle":[],"editor":[{"given":"Nur Ezlin","family":"Zamri","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2025,1]]},"references-count":59,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["10.1155\/acis\/8709881"],"URL":"https:\/\/doi.org\/10.1155\/acis\/8709881","archive":["Portico"],"relation":{},"ISSN":["1687-9724","1687-9732"],"issn-type":[{"value":"1687-9724","type":"print"},{"value":"1687-9732","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1]]},"assertion":[{"value":"2024-12-30","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-10-21","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-11-28","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"8709881"}}