Fatty Liver Disease Prediction Model Based on Big Data of Electronic Physical Examination Records

Zhao, Mingqi; Changjun, Song; Luo, Tao; Huang, Tianyue; Lin, Shi‐Ming

doi:10.3389/fpubh.2021.668351

Cited by 7 publications

(3 citation statements)

References 23 publications

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“…The use of big data may avoid overfitting the trained model. Despite the utilization of a large dataset for model training in a previous study [ 33 ], there was no comparison of the model’s performance with that of an existing validated index as in the present study. The use of the developed model may require validation before its application in clinical practice.…”

Section: Discussionmentioning

confidence: 91%

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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: Discussionmentioning

confidence: 91%

“…This model adds a regular term to the cost function to control the complexity of the model, simplifying it and preventing overfitting with improved training speed. In addition, xgBoost is a model based on the decision tree model, and it is more explanatory than neural networks and other algorithms are [ 33 ].…”

Section: Discussionmentioning

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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“…Participants were randomly assigned to the training and internal validation sets in a 7:3 ratio [ 29 , 30 ]. In addition, to further validate the performance of the prediction model, we used the follow-up population from 2015 to 2020 as an external validation set.…”

Section: Methodsmentioning

confidence: 99%

Predicting the 5-Year Risk of Nonalcoholic Fatty Liver Disease Using Machine Learning Models: Prospective Cohort Study

Huang,

Jin,

Mao

2023

J Med Internet Res

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Background Nonalcoholic fatty liver disease (NAFLD) has emerged as a worldwide public health issue. Identifying and targeting populations at a heightened risk of developing NAFLD over a 5-year period can help reduce and delay adverse hepatic prognostic events. Objective This study aimed to investigate the 5-year incidence of NAFLD in the Chinese population. It also aimed to establish and validate a machine learning model for predicting the 5-year NAFLD risk. Methods The study population was derived from a 5-year prospective cohort study. A total of 6196 individuals without NAFLD who underwent health checkups in 2010 at Zhenhai Lianhua Hospital in Ningbo, China, were enrolled in this study. Extreme gradient boosting (XGBoost)–recursive feature elimination, combined with the least absolute shrinkage and selection operator (LASSO), was used to screen for characteristic predictors. A total of 6 machine learning models, namely logistic regression, decision tree, support vector machine, random forest, categorical boosting, and XGBoost, were utilized in the construction of a 5-year risk model for NAFLD. Hyperparameter optimization of the predictive model was performed in the training set, and a further evaluation of the model performance was carried out in the internal and external validation sets. Results The 5-year incidence of NAFLD was 18.64% (n=1155) in the study population. We screened 11 predictors for risk prediction model construction. After the hyperparameter optimization, CatBoost demonstrated the best prediction performance in the training set, with an area under the receiver operating characteristic (AUROC) curve of 0.810 (95% CI 0.768-0.852). Logistic regression showed the best prediction performance in the internal and external validation sets, with AUROC curves of 0.778 (95% CI 0.759-0.794) and 0.806 (95% CI 0.788-0.821), respectively. The development of web-based calculators has enhanced the clinical feasibility of the risk prediction model. Conclusions Developing and validating machine learning models can aid in predicting which populations are at the highest risk of developing NAFLD over a 5-year period, thereby helping delay and reduce the occurrence of adverse liver prognostic events.

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A descriptive study of fatty liver disease detection using machine learning

Singh,

Thakral,

Kaur

2023

International Conference on Applied Computational Intelligence and Analytics (Acia-2022)

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Fatty Liver Disease Prediction Model Based on Big Data of Electronic Physical Examination Records

Cited by 7 publications

References 23 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

Predicting the 5-Year Risk of Nonalcoholic Fatty Liver Disease Using Machine Learning Models: Prospective Cohort Study

A descriptive study of fatty liver disease detection using machine learning

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