{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T03:16:28Z","timestamp":1771038988583,"version":"3.50.1"},"reference-count":33,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T00:00:00Z","timestamp":1766102400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T00:00:00Z","timestamp":1766102400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Int J Imaging Syst Tech"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Heart disease (HD) is still a major cause of death worldwide, which emphasizes the importance of early and precise prediction. This paper presents DenseEchoNet, a deep learning model that has been optimized with the Gazelle Optimizer Algorithm (GOA). The hybrid HD\u2010ENN technique is used for balanced learning to solve class imbalance and high dimensionality, while squared exponential kernel\u2010based PCA (SEKPCA) effectively reduces dimensionality. DenseEchoNet outperforms current baseline models with accuracies of 0.9795 as well as 0.9785, respectively, when tested on the HDHI and Cleveland datasets. XAI approaches, such as LIME and SHAP, improve model interpretability by offering distinct insights on feature contributions to HD risk. For early HD prediction, this system provides a straightforward, accurate, and efficient solution.<\/jats:p>","DOI":"10.1002\/ima.70268","type":"journal-article","created":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T08:35:22Z","timestamp":1766133322000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Deep Learning\u2010Based\n                    <scp>DenseEchoNet<\/scp>\n                    Framework With\n                    <scp>eXplainable<\/scp>\n                    Artificial Intelligence for Accurate and Early Heart Disease Prediction"],"prefix":"10.1002","volume":"36","author":[{"given":"Meghavathu S. S.","family":"Nayak","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering VIT\u2010AP University  Amaravati Andhra Pradesh India"}]},{"given":"Hussain","family":"Syed","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering VIT\u2010AP University  Amaravati Andhra Pradesh India"}]}],"member":"311","published-online":{"date-parts":[[2025,12,19]]},"reference":[{"key":"e_1_2_12_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-024-10899-9"},{"key":"e_1_2_12_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11883-024-01190-x"},{"key":"e_1_2_12_4_1","doi-asserted-by":"publisher","DOI":"10.32996\/bjns.2024.4.2.5"},{"key":"e_1_2_12_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2024.106269"},{"key":"e_1_2_12_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2024.03.048"},{"key":"e_1_2_12_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/s42979-025-03696-w"},{"key":"e_1_2_12_8_1","doi-asserted-by":"publisher","DOI":"10.3390\/a17100443"},{"key":"e_1_2_12_9_1","doi-asserted-by":"publisher","DOI":"10.3390\/bdcc7030144"},{"key":"e_1_2_12_10_1","doi-asserted-by":"publisher","DOI":"10.3390\/a16060308"},{"issue":"1","key":"e_1_2_12_11_1","article-title":"Leveraging Machine Learning for Predictive Models in Healthcare to Enhance Patient Outcome Management","volume":"7","author":"Kehinde A. 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