Improving accuracy of vascular access quality classification in hemodialysis patients using deep learning with K highest score feature selection.

Journal: The Journal of international medical research

PMID: 38573764

Abstract

OBJECTIVE: To develop and evaluate a novel feature selection technique, using photoplethysmography (PPG) sensors, for enhancing the performance of deep learning models in classifying vascular access quality in hemodialysis patients.

Authors

Sarayut Julkaew

College of Digital Science, Prince of Songkla University, Hat Yai, Songkhla, Thailand.
Thakerng Wongsirichot

Division of Computational Science, Faculty of Science, Prince of Songkla University, Hat Yai, Songkhla, Thailand. thakerng.w@psu.ac.th.
Kasikrit Damkliang
Pornpen Sangthawan

Department of Medicine, Division of Nephrology, Faculty of Medicine, Prince of Songkla University, Hat Yai, Songkhla, Thailand.

Keywords

Constriction, Pathologic Cross-Sectional Studies Deep Learning Humans Quality of Life Renal Dialysis

External Resources

View on PubMed Access via DOI PubMed (38573764)

Improving accuracy of vascular access quality classification in hemodialysis patients using deep learning with K highest score feature selection.

Abstract

Authors

Keywords

External Resources

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