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

Yang Niu^1,2, Xue Tao³, Qinyuan Chang³

¹Department of Rehabilitation Medicine, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China.

Peerj. Computer Science

|March 10, 2025

Summary

This summary is machine-generated.

Machine learning enhances cerebral infarction screening using speech and cognitive data. The developed framework accurately identifies cerebral infarction and its subtypes, improving early detection.

Keywords:

Cerebral infarction screening Feature selection Machine learning Speech and cognitive function assessment

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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Area of Science:

Neurology
Artificial Intelligence
Medical Diagnostics

Background:

Cerebral infarction screening (CIS) is crucial for prompt treatment and better patient outcomes.
Machine learning (ML) offers potential for improving diagnostic accuracy in neurological conditions.

Purpose of the Study:

To develop and evaluate a machine learning framework (CIS) for enhanced cerebral infarction screening.
To utilize speech and cognitive function features for classifying cerebral infarction subtypes.

Main Methods:

Analysis of a dataset with 117 patients (cerebral infarction, lacunar infarction, healthy controls).
Application of Recursive Feature Elimination with Cross-Validation (RFECV) for feature selection.
Evaluation of various ML classifiers including XGBoost for binary and ternary classification.

Main Results:

The CIS framework achieved 88.89% accuracy in distinguishing cerebral infarction from controls.
The system reached 77.78% accuracy in classifying lacunar infarction, non-lacunar infarction, and healthy controls.
Selected speech and cognitive features proved significant for differentiating cerebral infarction subtypes.

Conclusions:

Machine learning effectively enhances cerebral infarction screening using speech and cognitive data.
The developed CIS framework shows promise for improving early detection and diagnosis of cerebral infarction subtypes.
Integration of ML into clinical practice could advance neurological diagnostics.