Comparison and development of advanced machine learning tools to predict nonalcoholic fatty liver disease: An extended study

Liu, Yuan; Liu, Xi; Cen, Chao; Li, Xin; Liu, Jimin; Ming, Zhaoyan; Yu, Songfeng; Tang, Xiaofeng; Zhou, Lin; Yu, Jun; Huang, Kejie; Zheng, Shusen

doi:10.1016/j.hbpd.2021.08.004

Cited by 41 publications

(35 citation statements)

References 28 publications

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“…We compared the predictive capability of seven advanced machine-learning methods and confirmed that the xgBoost model demonstrated the best performance, with the highest AUROC (0.882). High accuracy was found in previous studies utilizing machine-learning methods [14,15,31,32], and the xgBoost model achieved better performance in our study. The xgBoost model has many advantages over other machine-learning models.…”

Section: Discussionsupporting

confidence: 76%

“…Several ML techniques, such as logistic regression (LR), random forest (RF), artificial neural networks (ANNs), support vector machines, and extreme gradient boosting (xgBoost), show promise in improving predictions compared with conventional risk scoring systems. There are several previous studies that used ML methods to show a higher diagnostic value for the presence of fatty liver disease with clinical variables [ 9 , 10 , 11 , 12 , 13 , 14 , 15 ]. However, these studies utilized a limited number of datasets, and most of them did not examine with an additional testing dataset for validation.…”

Section: Introductionmentioning

confidence: 99%

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Machine-Learning Algorithm for Predicting Fatty Liver Disease in a Taiwanese Population

Chen

Lin

Yen

et al. 2022

JPM

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The rising incidence of fatty liver disease (FLD) poses a health challenge, and is expected to be the leading global cause of liver-related morbidity and mortality in the near future. Early case identification is crucial for disease intervention. A retrospective cross-sectional study was performed on 31,930 Taiwanese subjects (25,544 training and 6386 testing sets) who had received health check-ups and abdominal ultrasounds in Changhua Christian Hospital from January 2009 to January 2019. Clinical and laboratory factors were included for analysis by different machine-learning algorithms. In addition, the performance of the machine-learning algorithms was compared with that of the fatty liver index (FLI). Totally, 6658/25,544 (26.1%) and 1647/6386 (25.8%) subjects had moderate-to-severe liver disease in the training and testing sets, respectively. Five machine-learning models were examined and demonstrated exemplary performance in predicting FLD. Among these models, the xgBoost model revealed the highest area under the receiver operating characteristic (AUROC) (0.882), accuracy (0.833), F1 score (0.829), sensitivity (0.833), and specificity (0.683) compared with those of neural network, logistic regression, random forest, and support vector machine-learning models. The xgBoost, neural network, and logistic regression models had a significantly higher AUROC than that of FLI. Body mass index was the most important feature to predict FLD according to the feature ranking scores. The xgBoost model had the best overall prediction ability for diagnosing FLD in our study. Machine-learning algorithms provide considerable benefits for screening candidates with FLD.

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Section: Discussionsupporting

confidence: 76%

Section: Introductionmentioning

confidence: 99%

Machine-Learning Algorithm for Predicting Fatty Liver Disease in a Taiwanese Population

Chen

Lin

Yen

et al. 2022

JPM

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“…We performed a meta-analysis of six studies incorporating AI into clinical data sets for NAFLD diagnosis. 28 – 33 Examples of clinical data sets primarily included demographic data (age, sex, weight, and height) and laboratory values (liver and renal function tests, lipid profile, and plasma glucose). Multiple AI classifiers were used in four studies, 28 – 30 , 33 while the other two studies used a single AI classifier (1 ANN 32 and 1 random forest 31 ).…”

Section: Resultsmentioning

confidence: 99%

“… 28 – 33 Examples of clinical data sets primarily included demographic data (age, sex, weight, and height) and laboratory values (liver and renal function tests, lipid profile, and plasma glucose). Multiple AI classifiers were used in four studies, 28 – 30 , 33 while the other two studies used a single AI classifier (1 ANN 32 and 1 random forest 31 ). Five articles selected ultrasonography as the diagnostic method, 28 – 30 , 32 , 33 while one study relied on MRI.…”

Section: Resultsmentioning

confidence: 99%

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Application of artificial intelligence in non-alcoholic fatty liver disease and liver fibrosis: a systematic review and meta-analysis

Decharatanachart

Chaiteerakij

Tiyarattanachai

et al. 2021

Therap Adv Gastroenterol

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Background: The global prevalence of non-alcoholic fatty liver disease (NAFLD) continues to rise. Non-invasive diagnostic modalities including ultrasonography and clinical scoring systems have been proposed as alternatives to liver biopsy but with limited performance. Artificial intelligence (AI) is currently being integrated with conventional diagnostic methods in the hopes of performance improvements. We aimed to estimate the performance of AI-assisted systems for diagnosing NAFLD, non-alcoholic steatohepatitis (NASH), and liver fibrosis. Methods: A systematic review was performed to identify studies integrating AI in the diagnosis of NAFLD, NASH, and liver fibrosis. Pooled sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and summary receiver operating characteristic curves were calculated. Results: Twenty-five studies were included in the systematic review. Meta-analysis of 13 studies showed that AI significantly improved the diagnosis of NAFLD, NASH and liver fibrosis. AI-assisted ultrasonography had excellent performance for diagnosing NAFLD, with a sensitivity, specificity, PPV, NPV of 0.97 (95% confidence interval (CI): 0.91–0.99), 0.98 (95% CI: 0.89–1.00), 0.98 (95% CI: 0.93–1.00), and 0.95 (95% CI: 0.88–0.98), respectively. The performance of AI-assisted ultrasonography was better than AI-assisted clinical data sets for the identification of NAFLD, which provided a sensitivity, specificity, PPV, NPV of 0.75 (95% CI: 0.66–0.82), 0.82 (95% CI: 0.74–0.88), 0.75 (95% CI: 0.60–0.86), and 0.82 (0.74–0.87), respectively. The area under the curves were 0.98 and 0.85 for AI-assisted ultrasonography and AI-assisted clinical data sets, respectively. AI-integrated clinical data sets had a pooled sensitivity, specificity of 0.80 (95%CI: 0.75–0.85), 0.69 (95%CI: 0.53–0.82) for identifying NASH, as well as 0.99–1.00 and 0.76–1.00 for diagnosing liver fibrosis stage F1–F4, respectively. Conclusion: AI-supported systems provide promising performance improvements for diagnosing NAFLD, NASH, and identifying liver fibrosis among NAFLD patients. Prospective trials with direct comparisons between AI-assisted modalities and conventional methods are warranted before real-world implementation. Protocol registration: PROSPERO (CRD42021230391)

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Diagnosis of Denizen Cirrhosis Disorders Using Supervised Machine Learning Classifiers

Patel

Shah

Patel

et al. 2022

Proceedings of Seventh International Congress on Information and Communication Technology

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Comparison and development of advanced machine learning tools to predict nonalcoholic fatty liver disease: An extended study

Cited by 41 publications

References 28 publications

Machine-Learning Algorithm for Predicting Fatty Liver Disease in a Taiwanese Population

Machine-Learning Algorithm for Predicting Fatty Liver Disease in a Taiwanese Population

Application of artificial intelligence in non-alcoholic fatty liver disease and liver fibrosis: a systematic review and meta-analysis

Diagnosis of Denizen Cirrhosis Disorders Using Supervised Machine Learning Classifiers

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