comparative performance of machine learning models in ischemic stroke classification

Background: 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).methods: 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.results: 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.conclusion: 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.