Machine learning to predict mitochondrial diseases by phenotypes
Journal
Mitochondrion
Journal Volume
84
Start Page
102061
ISSN
1567-7249
Date Issued
2025-09
Author(s)
Kuo, Chieh-Wen
Wu, Chao-Szu
Hsu, Ching
Lee, Ming-Jen
Lin, Ru-Jen
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.
Subjects
Machine learning
Mitochondrial diseases
Phenotype
SDGs
Type
journal article