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Open Access

Peer-reviewed

Research Article

Machine learning prediction of metabolic-associated fatty liver disease in type 2 diabetes: Emphasizing data imputation and feature selection

Zahra Khosravi,

Roles Conceptualization, Methodology, Software, Validation, Visualization, Writing – original draft

Affiliation School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
⨯
Farnaz Barzinpour ,

Roles Conceptualization, Methodology, Supervision, Validation, Writing – review & editing

* E-mail: barzinpour@iust.ac.ir

Affiliation School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran

https://orcid.org/0000-0001-9692-4432

⨯
Soghra Rabizadeh,

Roles Conceptualization, Data curation, Supervision, Validation, Writing – review & editing

Affiliation Endocrinology and Metabolism Research Center (EMRC), Imam Khomeini Hospital Complex, Vali-Asr Hospital, Tehran University of Medical Sciences, Tehran, Iran
⨯
Manouchehr Nakhjavani,

Roles Conceptualization, Data curation, Validation, Writing – review & editing

Affiliation Endocrinology and Metabolism Research Center (EMRC), Imam Khomeini Hospital Complex, Vali-Asr Hospital, Tehran University of Medical Sciences, Tehran, Iran
⨯
Alireza Esteghamati

Roles Conceptualization, Data curation, Validation, Writing – review & editing

Affiliation Endocrinology and Metabolism Research Center (EMRC), Imam Khomeini Hospital Complex, Vali-Asr Hospital, Tehran University of Medical Sciences, Tehran, Iran
⨯

Machine learning prediction of metabolic-associated fatty liver disease in type 2 diabetes: Emphasizing data imputation and feature selection

Zahra Khosravi,
Farnaz Barzinpour,
Soghra Rabizadeh,
Manouchehr Nakhjavani,
Alireza Esteghamati

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Published: February 24, 2026
https://doi.org/10.1371/journal.pone.0339580

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Type 2 diabetes
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Machine learning
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Fatty liver
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Boosting algorithms
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Machine learning algorithms
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Principal component analysis
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Preprocessing
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Ultrasound imaging
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