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The objective is to evaluate how RS and PCA contribute to the consistency and reliability of triage predictions when integrated with established ML algorithms. A dataset of 55,680 outpatient records was used to assess the integration of RS and PCA with five supervised ML models: Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), Logistic Regression (LR), and Na\u00efve Bayes (NB). Performance was evaluated using accuracy, precision, recall, and F-score metrics. The evaluation reveals that each preprocessing technique affects model behaviour in distinct ways, and the RS Decision Tree combination exhibits stable performance in generating triage outcomes. PCA-based models exhibit characteristic patterns associated with dimensionality reduction, affecting interpretability and model response. The findings emphasize the role of preprocessing techniques in shaping ML-driven telemedicine workflows. Applying RS within ML pipelines supports consistent triage prediction, contributing to timely identification of patient conditions and strengthening data-driven remote healthcare services.<\/jats:p>","DOI":"10.1007\/s13721-026-00740-4","type":"journal-article","created":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T10:37:44Z","timestamp":1771324664000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Evaluation of triage performance in IoMT-based telemedicine using robust scaler and PCA preprocessing"],"prefix":"10.1007","volume":"15","author":[{"given":"Omar Sadeq","family":"Salman","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nurul Muazzah Abdul","family":"Latiff","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sharifah Hafizah Syed","family":"Ariffin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Omar. 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