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. 2023 Sep 14;13(18):2952.
doi: 10.3390/diagnostics13182952.

A Machine Learning-Based Method for Detecting Liver Fibrosis

Miguel Suárez  1   2   3 , Raquel Martínez  1   3 , Ana María Torres  2   3 , Antonio Ramón  4 , Pilar Blasco  4 , Jorge Mateo  2   3
Affiliations

A Machine Learning-Based Method for Detecting Liver Fibrosis

Miguel Suárez et al. Diagnostics (Basel). .

Abstract

Cholecystectomy and Metabolic-associated steatotic liver disease (MASLD) are prevalent conditions in gastroenterology, frequently co-occurring in clinical practice. Cholecystectomy has been shown to have metabolic consequences, sharing similar pathological mechanisms with MASLD. A database of MASLD patients who underwent cholecystectomy was analysed. This study aimed to develop a tool to identify the risk of liver fibrosis after cholecystectomy. For this purpose, the extreme gradient boosting (XGB) algorithm was used to construct an effective predictive model. The factors associated with a better predictive method were platelet level, followed by dyslipidaemia and type-2 diabetes (T2DM). Compared to other ML methods, our proposed method, XGB, achieved higher accuracy values. The XGB method had the highest balanced accuracy (93.16%). XGB outperformed KNN in accuracy (93.16% vs. 84.45%) and AUC (0.92 vs. 0.84). These results demonstrate that the proposed XGB method can be used as an automatic diagnostic aid for MASLD patients based on machine-learning techniques.

Keywords: artificial intelligence; cholecystectomy; liver fibrosis; machine learning.

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Conflict of interest statement

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Figures

Figure 1
Figure 1
Diagram of how the machine learning method was performed.
Figure 2
Figure 2
Representation of the most important variables and their value in the predictive model. Abbreviations. FIB-4: Fibrosis-4; T2DM: Type-2 Diabetes Mellitus; BMI: Body Mass Index; HDL: High-Density Lipoprotein cholesterol.
Figure 3
Figure 3
Representation of ROC curve of all compared methods. Abbreviations. ROC: Receiver Operating Characteristic; BLDA: Bayesian Linear Discriminate Analysis; SVM: Support-Vector Machine; LR: logistic regression; DT: Decision Tree; KNN: K-Nearest Neighbour; XGB: eXtreme Gradient Boost.
Figure 4
Figure 4
Radar plot of all compared methods. The train phase is represented on the left side of the image, while the test phase is drawn on the right. Abbreviations. BLDA: Bayesian Linear Discriminate Analysis; SVM: Support-Vector Machine; LR: logistic regression; DT: Decision Tree; KNN: K-Nearest Neighbour; XGB: eXtreme Gradient Boost; AUC: Area Under the Curve; MCC: Matthews Correlation Coefficient.
See this image and copyright information in PMC

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