Machine learning to predict mitochondrial diseases by phenotypes

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.

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Publication Details

Journal
Mitochondrion
Published
2025-06-19
DOI
https://doi.org/10.1016/j.mito.2025.102061
Citations
2
Primary Topic
Mitochondrial Function and Pathology
Type
article
Field-Weighted Citation Impact
1.61
Controls
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article

Machine learning to predict mitochondrial diseases by phenotypes

Yin‐Hsiu Chien, Ming‐Jen Lee, Hsueh‐Wen Hsueh, Chao-Szu Wu et al.
2 citations
Mitochondrion
Mitochondrial Function and Pathology
1.61
article

Machine learning to predict mitochondrial diseases by phenotypes

Yin‐Hsiu Chien, Ming‐Jen Lee, Hsueh‐Wen Hsueh, Chao-Szu Wu, Hui‐An Chen, Pi‐Chuan Fan, Wuh‐Liang Hwu, Wang‐Tso Lee, Ni‐Chung Lee, Ta‐Ching Chen, Chih‐Chao Yang, Wen‐Chin Weng, Rai-Hseng Hsu, C.C. Kuo, Ching Hsu, Ru-Jen Lin, Feng-Jung Yang
article en
2 citations

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.

MitochondrionVol. 84
China Medical University (TW), China Medical University Hospital (TW), National Taiwan University Hospital (TW)
Openalex Percentile: Top 16%
Mitochondrial Function and Pathology
1.61
DOIOpen Access PDFOpenAlex
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