{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T14:14:09Z","timestamp":1776867249113,"version":"3.51.2"},"reference-count":35,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T00:00:00Z","timestamp":1755216000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>Cardiovascular Disease (CVD) remains the number one cause of morbidity and mortality, accounting for 17.9 million deaths every year. Precise and early diagnosis is therefore critical to the betterment of the patient\u2019s outcomes and the many burdens that weigh on the healthcare systems. This work presents for the first time an innovative approach using the DenseNet architecture that allows for the automatic recognition of CVD from clinical data. The data is preprocessed and augmented, with a heterogeneous dataset of cardiovascular-related images like angiograms, echocardiograms, and magnetic resonance images used. Optimizing the deep features for robust model performance is conducted through fine-tuning a custom DenseNet architecture along with rigorous hyper parameter tuning and sophisticated strategies to handle class imbalance. The DenseNet model, after training, shows high accuracy, sensitivity, and specificity in the identification of CVD compared to baseline approaches. Apart from the quantitative measures, detailed visualizations are conducted to show that the model is able to localize and classify pathological areas within an image. The accuracy of the model was found to be 0.92, precision 0.91, and recall 0.95 for class 1, and an overall weighted average F1-score of 0.93, which establishes the efficacy of the model. There is great clinical applicability in this research in terms of accurate detection of CVD to provide time-interventional personalized treatments. This DenseNet-based approach advances the improvement on the diagnosis of CVD through state-of-the-art technology to be used by radiologists and clinicians. Future work, therefore, would probably focus on improving the model\u2019s interpretability towards a broader population of patients and its generalization towards it, revolutionizing the diagnosis and management of CVD.<\/jats:p>","DOI":"10.3390\/computers14080330","type":"journal-article","created":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T10:33:42Z","timestamp":1755254022000},"page":"330","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Enhancing Cardiovascular Disease Detection Through Exploratory Predictive Modeling Using DenseNet-Based Deep Learning"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7575-9287","authenticated-orcid":false,"given":"Wael","family":"Hadi","sequence":"first","affiliation":[{"name":"Information Security Department, University of Petra, Amman 961343, Jordan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8221-2880","authenticated-orcid":false,"given":"Tushar","family":"Jaware","sequence":"additional","affiliation":[{"name":"Department of Electronics and Telecommunication Engineering, R. C. Patel Institute of Technology, Shirpur 425405, Maharashtra, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0467-805X","authenticated-orcid":false,"given":"Tarek","family":"Khalifa","sequence":"additional","affiliation":[{"name":"College of Engineering and Technology, American University of the Middle East, Eqaila 54200, Kuwait"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Faisal","family":"Aburub","sequence":"additional","affiliation":[{"name":"Business Intelligence and Data Analytics Department, University of Petra, Amman 961343, Jordan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2758-4275","authenticated-orcid":false,"given":"Nawaf","family":"Ali","sequence":"additional","affiliation":[{"name":"College of Engineering and Technology, American University of the Middle East, Eqaila 54200, Kuwait"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rashmi","family":"Saini","sequence":"additional","affiliation":[{"name":"G. B. Pant Institute of Engineering and Technology, Pauri Garhwal 246196, Uttarakhand,  India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,15]]},"reference":[{"key":"ref_1","first-page":"665","article-title":"An efficient stacked ensemble model for heart disease detection and classification","volume":"77","author":"Abbas","year":"2023","journal-title":"Comput. Mater. Contin."},{"key":"ref_2","unstructured":"Arvaniti, E., Claassen, M., and Giamberardino, G. (2017). Automated cardiac diagnosis challenge (ACDC) & caudate nucleus segmentation challenge\u2014Preliminary results. Statistical Atlases and Computational Models of the Heart. ACDC and MMWHS Challenges, Springer."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Asif, D., Bibi, M., Arif, M.S., and Mukheimer, A. (2023). Enhancing heart disease prediction through ensemble learning techniques with hyperparameter optimization. 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