{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T09:11:01Z","timestamp":1765357861331,"version":"3.41.0"},"reference-count":0,"publisher":"Advances in Artificial Intelligence and Machine Learning","issue":"02","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAIML"],"published-print":{"date-parts":[[2025]]},"abstract":"<jats:p>Financial distress prediction remains fundamental to identifying troubled businesses since it\ndetermines business stability along with economic forecast accuracy. The research evaluates\nthe Synthetic Minority Over-sampling Technique (SMOTE) to correct class imbalance issues\nin financial distress prediction by studying its results when standardized through clustering\nand non-clustering approaches. The research determines how K-means clustering strengthens SMOTE by applying data balancing techniques inside separate clusters to improve model\npredictions for financial distress. Combining K-means clustering with SMOTE substantially\nimproves model performance because XGBoost demonstrates the peak results, including\n99% accuracy and 99% F1 score. Incorporating clustering methods helps SMOTE produce\nmore accurate synthetic samples, achieving better predictive accuracy by improving class\nbalance. According to these results, combining clustering methods and SMOTE demonstrates great potential for financial distress prediction in imbalanced datasets.<\/jats:p>","DOI":"10.54364\/aaiml.2025.52208","type":"journal-article","created":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T10:38:56Z","timestamp":1751366336000},"page":"3663-3681","source":"Crossref","is-referenced-by-count":1,"title":["Improving Financial Distress Prediction through Clustered SMOTE for Imbalanced Data"],"prefix":"10.54364","volume":"05","author":[{"given":"Kalina","family":"Kitova","sequence":"first","affiliation":[]},{"given":"Borislava","family":"Toleva","sequence":"additional","affiliation":[]},{"given":"Ivan","family":"Ivanov","sequence":"additional","affiliation":[]}],"member":"32807","published-online":{"date-parts":[[2025]]},"container-title":["Advances in Artificial Intelligence and Machine Learning"],"original-title":[],"link":[{"URL":"https:\/\/www.oajaiml.com\/uploads\/archivepdf\/715252208.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T10:50:15Z","timestamp":1751367015000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.oajaiml.com\/uploads\/archivepdf\/715252208.pdf"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":0,"journal-issue":{"issue":"02","published-online":{"date-parts":[[2025]]},"published-print":{"date-parts":[[2025]]}},"URL":"https:\/\/doi.org\/10.54364\/aaiml.2025.52208","relation":{},"ISSN":["2582-9793"],"issn-type":[{"value":"2582-9793","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]}}}