en علوم پایه پزشکی Basic Medical Sciences تحقیقی Original article <div style="text-align: justify;"><span style="font-size:12px;"><span style="font-family:Times New Roman;"><b>Background:</b> Stroke is a leading cause of disability and mortality worldwide, with ischemic strokes comprising the majority of cases. Despite advances in neuroimaging, there is a pressing need for supplementary diagnostic tools to enhance accuracy. This study explores the application of machine learning (ML) techniques to predict ischemic stroke using RNA-seq data from the GEO database (GSE22255).<br> <b>Methods:</b> We developed and evaluated various machine learning models, including Random Forest, K-Nearest Neighbors (KNN), and CHAID (Chi-squared Automatic Interaction Detection), based on their accuracy, precision, specificity, and sensitivity. The analysis utilized a dataset comprising 54,676 genes across 40 samples (20 cases and 20 controls). All modeling was conducted using IBM SPSS Modeler version 18.<br> <b>Results:</b> The models were assessed based on their classification accuracy, performance evaluation scores, and AUC/Gini AUC metrics. The Random Forest model achieved the highest accuracy (96.67% in training, 80% in testing), while the CHAID algorithm provided interpretable results with key variables (TP53, CYP1A1, and CYP2D6) identified. The KNN model exhibited strong performance with notable confidence in its predictions.<br> <b>Conclusion</b>: This study demonstrates the potential of ML techniques, particularly Random Forest, to enhance stroke diagnosis and provide insights into stroke pathology, offering a novel approach to improving clinical decision-making. However, the study is limited by the small sample size, and future work should focus on validation with larger datasets and integration with other omics data for clinical application.</span></span></div> Ischemic Stroke, Machine Learning, Random Forest, Predictive Medicine, K-Nearest Neighbors 20 28 http://goums.ac.ir/jorjanijournal/browse.php?a_code=A-10-547-2&slc_lang=en&sid=1 Mina Rahmati mnrahmati69@gmail.com 100319475328460010533 100319475328460010533 No Pasteur Institute of Iran, Tehran, Iran Masoud Arabfard arabfard@gmail.com 100319475328460010534 100319475328460010534 Yes Artificial Intelligence in Health Research Center, Biomedicine Technologies Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran