{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T16:31:43Z","timestamp":1777653103451,"version":"3.51.4"},"reference-count":0,"publisher":"Slovenian Association Informatika","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJCAI"],"abstract":"<jats:p>Cardiovascular diseases remain the leading global cause of death, demanding diagnostic systems that are accurate, interpretable, and computationally efficient. Traditional machine learning approaches frequently struggle with class imbalance, high-dimensional noise, and restricted generalization in clinical datasets. To tackle such issues, we propose a hybrid framework that combines SVM\u2013SMOTE and neighborhood cleaning rule (NCL) for class rebalancing, a sparse autoencoder (SAE) with random forest (RF) selection for non-linear feature optimization, and a class-weighted multilayer perceptron (MLP) for final classification. We validate our framework on the Z-Alizadeh Sani (54 features) and Cleveland (13 features) datasets under stratified fivefold cross-validation, the model attains mean accuracies of 94.02 \u00b1 2.77 % and 94.36 \u00b1 1.47 %, with AUC\u2013ROC = 0.988 and 0.982, outperforming prior baselines [4, 10, 14] by 7.6%\u201320.8%, and Bootstrap 95% confidence intervals and McNemar\/DeLong tests (p &lt; 0.001) confirms significance. Noteably, the ablation study demonstrates the contribution of each module (e.g., a 12% accuracy improvement without sampling). The optimized MLP reduced false negatives to ~5%, while training 40% faster than CNN\u2013LSTM alternatives. The proposed framework provides a statistically robust and interpretable solution for predicting cardiovascular disease.<\/jats:p>","DOI":"10.31449\/inf.v50i5.10455","type":"journal-article","created":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T10:25:40Z","timestamp":1770027940000},"source":"Crossref","is-referenced-by-count":1,"title":["Cardiovascular Disease Prediction via Hybrid SVM\u2013SMOTE and Sparse Autoencoder Feature Reduction with Deep MLP Classification"],"prefix":"10.31449","volume":"50","author":[{"given":"Zaid","family":"Alaa","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ali","family":"Sabah","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"16141","published-online":{"date-parts":[[2026,2,2]]},"container-title":["Informatica"],"original-title":[],"link":[{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/10455\/6429","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/10455\/6429","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T10:25:40Z","timestamp":1770027940000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/view\/10455"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,2]]},"references-count":0,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2026,2,2]]}},"URL":"https:\/\/doi.org\/10.31449\/inf.v50i5.10455","relation":{},"ISSN":["1854-3871","0350-5596"],"issn-type":[{"value":"1854-3871","type":"electronic"},{"value":"0350-5596","type":"print"}],"subject":[],"published":{"date-parts":[[2026,2,2]]}}}