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Prediction of Poststroke Depression Based on the Outcomes of Machine Learning Algorithms.

Yeong Hwan Ryu1, Seo Young Kim1, Tae Uk Kim1

  • 1Department of Rehabilitation Medicine, College of Medicine, Dankook University, Cheonan 31116, Korea.

Journal of Clinical Medicine
|April 23, 2022
PubMed
Summary

Machine learning accurately predicts poststroke depression (PSD) occurrence and prognosis in stroke patients. Cognitive and functional assessments are key predictors, outperforming traditional statistical methods.

Keywords:
cognitive scalefunctional scalemachine learningpoststroke depressionprediction

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Area of Science:

  • Neurology
  • Psychiatry
  • Artificial Intelligence

Background:

  • Poststroke depression (PSD) is a significant complication following stroke.
  • The impact of PSD treatment on cognitive and functional recovery remains unclear.
  • Predicting PSD occurrence and prognosis is crucial for patient management.

Purpose of the Study:

  • To investigate the efficacy of machine learning algorithms in predicting PSD.
  • To determine if cognitive and functional statuses can predict PSD occurrence and prognosis.
  • To compare machine learning predictive performance against traditional statistical methods.

Main Methods:

  • Utilized data from 31 PSD patients and 34 controls.
  • Performed neurological, cognitive (K-MMSE, CNT), and functional (K-MBI, FIM) assessments.
  • Applied machine learning (SVM, KNN, Random Forest, Voting Ensemble) and logistic regression.

Main Results:

  • Support Vector Machine (SVM) with RBF kernel predicted PSD occurrence (AUC=0.711, accuracy=0.700).
  • SVM linear algorithm predicted PSD prognosis (AUC=0.830, accuracy=0.771).
  • Machine learning algorithms demonstrated superior predictive performance over statistical methods.

Conclusions:

  • Machine learning effectively predicts the occurrence and prognosis of PSD in stroke patients.
  • Cognitive and functional statuses are significant predictors for PSD.
  • Machine learning offers a promising tool for early identification and management of PSD.