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

ABSTRACT: 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 infar

SUBMITTER: Niu Y 

PROVIDER: S-EPMC11888919 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature