Investigation of predictive factors for fatty liver in children and adolescents using artificial intelligence
BackgroundChildhood obesity is a growing problem worldwide, leading to non-alcoholic fatty liver disease (NAFLD), which is the most common liver disease in children. Liver biopsy is the gold standard for NAFLD diagnosis. Machine learning algorithms could assist in an early diagnostic approach and le...
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Frontiers Media S.A.
2025-08-01
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| Series: | Frontiers in Pediatrics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fped.2025.1537098/full |
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| author | Aliakbar Sayyari Amin Magsudy Yasamin Moeinipour Amirhossein Hosseini Hamidreza Amiri Mohammadreza Arzaghi Fereshteh Sohrabivafa Seyedeh Fatemeh Hamzavi Ashkan Azizi Tahereh Hatamii AmirAli Okhovat Naghi Dara Negar Imanzadeh Farid Imanzadeh Mahmoud Hajipour |
| author_facet | Aliakbar Sayyari Amin Magsudy Yasamin Moeinipour Amirhossein Hosseini Hamidreza Amiri Mohammadreza Arzaghi Fereshteh Sohrabivafa Seyedeh Fatemeh Hamzavi Ashkan Azizi Tahereh Hatamii AmirAli Okhovat Naghi Dara Negar Imanzadeh Farid Imanzadeh Mahmoud Hajipour |
| author_sort | Aliakbar Sayyari |
| collection | DOAJ |
| description | BackgroundChildhood obesity is a growing problem worldwide, leading to non-alcoholic fatty liver disease (NAFLD), which is the most common liver disease in children. Liver biopsy is the gold standard for NAFLD diagnosis. Machine learning algorithms could assist in an early diagnostic approach and leading to a favorable prognosis.ObjectiveThis study aimed to identify predictive factors for NAFLD in children and adolescents using machine learning models, focusing on liver biopsy outcomes such as fibrosis, infiltration, ballooning, and steatosis.MethodsData from 659 children suspected of NAFLD, who underwent liver biopsy at Mofid Children's Hospital between 2011 and 2023, were analyzed. The dataset included categorical and numerical variables, which were processed using one-hot encoding and standardization. Several machine learning models were trained and evaluated, including CatBoost, AdaBoost, Random Forest, and others. Model performance was assessed using cross-validation with accuracy, precision, recall, F1 score, and ROC-AUC metrics. Feature importance was determined through permutation analysis.ResultsAmong NAFLD patients, the CatBoost Classifier achieved the highest accuracy (91.8%) and ROC-AUC score (92.3%) in cross-validation. In addition, the adjusted models showed better results. That is, the F1 for the CatBoost raised from 83% to 89% (AUC: 0.86–0.92), for the GradientBoosting from 76% to 81% (AUC: 0.81–0.85), and for Bernolli Naive Bayes from 78% to 82% (AUC: 0.82–0.85).ConclusionMachine learning models, particularly CatBoost, demonstrated strong predictive capabilities for NAFLD diagnosis in children. |
| format | Article |
| id | doaj-art-66141c2edbc24ae5b23a544ae72bba32 |
| institution | Kabale University |
| issn | 2296-2360 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Pediatrics |
| spelling | doaj-art-66141c2edbc24ae5b23a544ae72bba322025-08-20T03:40:29ZengFrontiers Media S.A.Frontiers in Pediatrics2296-23602025-08-011310.3389/fped.2025.15370981537098Investigation of predictive factors for fatty liver in children and adolescents using artificial intelligenceAliakbar Sayyari0Amin Magsudy1Yasamin Moeinipour2Amirhossein Hosseini3Hamidreza Amiri4Mohammadreza Arzaghi5Fereshteh Sohrabivafa6Seyedeh Fatemeh Hamzavi7Ashkan Azizi8Tahereh Hatamii9AmirAli Okhovat10Naghi Dara11Negar Imanzadeh12Farid Imanzadeh13Mahmoud Hajipour14Pediatric Gastroenterology, Hepatology and Nutrition Research Center, Research Institute for Children’s Health, Shahid Beheshti University of Medical Sciences, Tehran, IranSchool of Medicine, Islamic Azad University, Tabriz Branch, Tabriz, IranCardiovascular Surgery, Cardiothoracic Surgery Department, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, IranPediatric Gastroenterology, Hepatology and Nutrition Research Center, Research Institute for Children’s Health, Shahid Beheshti University of Medical Sciences, Tehran, IranStudent Research Committee, Arak University of Medical Sciences, Arak, IranStudent Research Committee, Shahid Beheshti University of Medical Sciences, Tehran, IranDepartment of Community Medicine, School of Medicine, Dezful University of Medical Sciences, Dezful, IranSchool of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, IranSchool of Medicine, Tehran University of Medical Sciences, Tehran, IranSchool of Medicine, Tehran University of Medical Sciences, Tehran, IranPediatric Gastroenterology, Hepatology and Nutrition Research Center, Research Institute for Children’s Health, Shahid Beheshti University of Medical Sciences, Tehran, IranSchool of Pharmacy, Shahid Beheshti University of Medical Sciences, Tehran, IranSchool of Pharmacy, Shahid Beheshti University of Medical Sciences, Tehran, IranPediatric Gastroenterology, Hepatology and Nutrition Research Center, Research Institute for Children’s Health, Shahid Beheshti University of Medical Sciences, Tehran, IranPediatric Gastroenterology, Hepatology and Nutrition Research Center, Research Institute for Children’s Health, Shahid Beheshti University of Medical Sciences, Tehran, IranBackgroundChildhood obesity is a growing problem worldwide, leading to non-alcoholic fatty liver disease (NAFLD), which is the most common liver disease in children. Liver biopsy is the gold standard for NAFLD diagnosis. Machine learning algorithms could assist in an early diagnostic approach and leading to a favorable prognosis.ObjectiveThis study aimed to identify predictive factors for NAFLD in children and adolescents using machine learning models, focusing on liver biopsy outcomes such as fibrosis, infiltration, ballooning, and steatosis.MethodsData from 659 children suspected of NAFLD, who underwent liver biopsy at Mofid Children's Hospital between 2011 and 2023, were analyzed. The dataset included categorical and numerical variables, which were processed using one-hot encoding and standardization. Several machine learning models were trained and evaluated, including CatBoost, AdaBoost, Random Forest, and others. Model performance was assessed using cross-validation with accuracy, precision, recall, F1 score, and ROC-AUC metrics. Feature importance was determined through permutation analysis.ResultsAmong NAFLD patients, the CatBoost Classifier achieved the highest accuracy (91.8%) and ROC-AUC score (92.3%) in cross-validation. In addition, the adjusted models showed better results. That is, the F1 for the CatBoost raised from 83% to 89% (AUC: 0.86–0.92), for the GradientBoosting from 76% to 81% (AUC: 0.81–0.85), and for Bernolli Naive Bayes from 78% to 82% (AUC: 0.82–0.85).ConclusionMachine learning models, particularly CatBoost, demonstrated strong predictive capabilities for NAFLD diagnosis in children.https://www.frontiersin.org/articles/10.3389/fped.2025.1537098/fullNAFLDmachine learningpredictive factorschildhood obesityfibrosisCatBoost |
| spellingShingle | Aliakbar Sayyari Amin Magsudy Yasamin Moeinipour Amirhossein Hosseini Hamidreza Amiri Mohammadreza Arzaghi Fereshteh Sohrabivafa Seyedeh Fatemeh Hamzavi Ashkan Azizi Tahereh Hatamii AmirAli Okhovat Naghi Dara Negar Imanzadeh Farid Imanzadeh Mahmoud Hajipour Investigation of predictive factors for fatty liver in children and adolescents using artificial intelligence Frontiers in Pediatrics NAFLD machine learning predictive factors childhood obesity fibrosis CatBoost |
| title | Investigation of predictive factors for fatty liver in children and adolescents using artificial intelligence |
| title_full | Investigation of predictive factors for fatty liver in children and adolescents using artificial intelligence |
| title_fullStr | Investigation of predictive factors for fatty liver in children and adolescents using artificial intelligence |
| title_full_unstemmed | Investigation of predictive factors for fatty liver in children and adolescents using artificial intelligence |
| title_short | Investigation of predictive factors for fatty liver in children and adolescents using artificial intelligence |
| title_sort | investigation of predictive factors for fatty liver in children and adolescents using artificial intelligence |
| topic | NAFLD machine learning predictive factors childhood obesity fibrosis CatBoost |
| url | https://www.frontiersin.org/articles/10.3389/fped.2025.1537098/full |
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