Machine learning-based feature selection and classification for cerebral infarction screening: an experimental study.

Cerebral infarction screening (CIS) is critical for timely intervention and improved patient outcomes. We investigate the application of machine learning techniques for feature selection and classification of speech and cognitive function assessments to enhance cerebral infarction screening. We analyze a dataset containing 117 patients (95 patients were diagnosed with cerebral infarction, and 54 were identified as lacunar cerebral infarction of them) comprising speech and cognitive function features from patients with lacunar and non-lacunar cerebral infarction, as well as healthy controls. In this article, we present a framework called CIS which comprises a cerebral infarction screening model to identify cerebral infarction from populations and a diagnostic model to classify lacunar infarction, non-lacunar infarction, and healthy controls. Feature selection method, Recursive Feature Elimination with Cross-Validation (RFECV), is employed to identify the most relevant features. Various classifiers, such as support vector machine, K-nearest neighbor, decision tree, random forest, logistic regression, and eXtreme gradient boosting (XGBoost), were evaluated for their performance in binary and ternary classification tasks. The CIS based on XGBoost classifier achieved the highest accuracy of 88.89% in the binary classification task (