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A Machine Learning Classifier for Predicting Stable MCI Patients Using Gene Biomarkers

Author

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  • Run-Hsin Lin

    (Institute of Biotechnology and Pharmaceutical Research, National Health Research Institutes, Miaoli County 35053, Taiwan
    Graduate Institute of Data Science, College of Management, Taipei Medical University, Taipei 10675, Taiwan)

  • Chia-Chi Wang

    (Department and Graduate Institute of Veterinary Medicine, School of Veterinary Medicine, National Taiwan University, Taipei 10617, Taiwan)

  • Chun-Wei Tung

    (Institute of Biotechnology and Pharmaceutical Research, National Health Research Institutes, Miaoli County 35053, Taiwan
    Graduate Institute of Data Science, College of Management, Taipei Medical University, Taipei 10675, Taiwan)

Abstract

Alzheimer’s disease (AD) is a neurodegenerative disorder with an insidious onset and irreversible condition. Patients with mild cognitive impairment (MCI) are at high risk of converting to AD. Early diagnosis of unstable MCI patients is therefore vital for slowing the progression to AD. However, current diagnostic methods are either highly invasive or expensive, preventing their wide applications. Developing low-invasive and cost-efficient screening methods is desirable as the first-tier approach for identifying unstable MCI patients or excluding stable MCI patients. This study developed feature selection and machine learning algorithms to identify blood-sample gene biomarkers for predicting stable MCI patients. Two datasets obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database were utilized to conclude 29 genes biomarkers (31 probes) for predicting stable MCI patients. A random forest-based classifier performed well with area under the receiver operating characteristic curve (AUC) values of 0.841 and 0.775 for cross-validation and test datasets, respectively. For patients with a prediction score greater than 0.9, an excellent concordance of 97% was obtained, showing the usefulness of the proposed method for identifying stable MCI patients. In the context of precision medicine, the proposed prediction model is expected to be useful for identifying stable MCI patients and providing medical doctors and patients with new first-tier diagnosis options.

Suggested Citation

  • Run-Hsin Lin & Chia-Chi Wang & Chun-Wei Tung, 2022. "A Machine Learning Classifier for Predicting Stable MCI Patients Using Gene Biomarkers," IJERPH, MDPI, vol. 19(8), pages 1-9, April.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:8:p:4839-:d:795210
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    References listed on IDEAS

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    1. Chyn Liaw & Chun-Wei Tung & Shinn-Ying Ho, 2013. "Prediction and Analysis of Antibody Amyloidogenesis from Sequences," PLOS ONE, Public Library of Science, vol. 8(1), pages 1-15, January.
    2. Shan-Han Huang & Ying-Chi Lin & Chun-Wei Tung, 2020. "Identification of Time-Invariant Biomarkers for Non-Genotoxic Hepatocarcinogen Assessment," IJERPH, MDPI, vol. 17(12), pages 1-14, June.
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    Cited by:

    1. Zhiyuan Hao & Jie Ma & Wenjing Sun, 2022. "The Technology-Oriented Pathway for Auxiliary Diagnosis in the Digital Health Age: A Self-Adaptive Disease Prediction Model," IJERPH, MDPI, vol. 19(19), pages 1-23, September.
    2. Chien-Lung Chan & Chi-Chang Chang, 2022. "Big Data, Decision Models, and Public Health," IJERPH, MDPI, vol. 19(14), pages 1-9, July.

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