Machine learning to predict mitochondrial diseases by phenotypes.

Journal: Mitochondrion
Published Date: Jun 18, 2025

Abstract

Diagnosing mitochondrial diseases remains challenging because of the heterogeneous symptoms. This study aims to use machine learning to predict mitochondrial diseases from phenotypes to reduce genetic testing costs. This study included patients who underwent whole exome or mitochondrial genome sequencing for suspected mitochondrial diseases. Clinical phenotypes were coded, and machine learning models (support vector machine, random forest, multilayer perceptron, and XGBoost) were developed to classify patients. Of 103 patients, 43 (41.7%) had mitochondrial diseases. Myopathy and respiratory failure differed significantly between the two groups. XGBoost achieved the highest accuracy (67.5%). In conclusion, machine learning improves patient prioritization and diagnostic yield.

Authors

  • Chieh-Wen Kuo
    Department of Pediatrics, National Taiwan University Children's Hospital, Taipei, Taiwan.
  • Hui-An Chen
    Department of Pediatrics, National Taiwan University Children's Hospital, Taipei, Taiwan.
  • Rai-Hseng Hsu
    Department of Pediatrics, National Taiwan University Children's Hospital, Taipei, Taiwan; Department of Medical Genetics, National Taiwan University Hospital, Taipei, Taiwan.
  • Chao-Szu Wu
    Department of Medical Genetics, National Taiwan University Hospital, Taipei, Taiwan.
  • Ching Hsu
    Department of Medical Genetics, National Taiwan University Hospital, Taipei, Taiwan.
  • Ming-Jen Lee
    Department of Neurology, National Taiwan University Hospital, Taipei, Taiwan.
  • Yin-Hsiu Chien
    Department of Pediatrics, National Taiwan University Children's Hospital, Taipei, Taiwan; Department of Medical Genetics, National Taiwan University Hospital, Taipei, Taiwan; National Taiwan University College of Medicine, Taipei, Taiwan.
  • Hsueh-Wen Hsueh
    Department of Neurology, National Taiwan University Hospital, Taipei, Taiwan.
  • Feng-Jung Yang
    Department of Internal Medicine, National Taiwan University Hospital Yunlin Branch, Douliu City, Yunlin County; School of Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan, ROC. Electronic address: fongrong@ntu.edu.tw.
  • Pi-Chuan Fan
    Department of Pediatrics, National Taiwan University Children's Hospital, Taipei, Taiwan.
  • Wen-Chin Weng
    Department of Pediatrics, National Taiwan University Hospital, and College of Medicine, National Taiwan University, Taipei, Taiwan; Department of Pediatric Neurology, National Taiwan University Children's Hospital, Taipei, Taiwan.
  • Ru-Jen Lin
    National Taiwan University Hospital Hsin-Chu Branch, Hsin-Chu, Taiwan.
  • Ta-Ching Chen
    Department of Ophthalmology, National Taiwan University Hospital, Taipei, Taiwan.
  • Chih-Chao Yang
    Department of Neurology, National Taiwan University Hospital, Taipei, Taiwan.
  • Wang-Tso Lee
    Department of Pediatrics, National Taiwan University Children's Hospital, Taipei, Taiwan.
  • Wuh-Liang Hwu
    Department of Medical Genetics, National Taiwan University Hospital, Taipei, Taiwan; Center for Precision Medicine, China Medical University Hospital, Taichung City, Taiwan.
  • Ni-Chung Lee
    Department of Pediatrics and Medical Genetics, National Taiwan University Hospital, Taipei, Taiwan.

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