Steatotic liver index: An interpretable predictor of steatotic liver disease using machine learning with an enhanced shrinkage method

Abstract

Aim

While the Fatty Liver Index (FLI) has been the most prominent among interpretable predictors for steatotic liver disease (SLD), we aimed to prepare a novel diagnostic/prognostic index better than FLI for SLD using a non-black-box and modified parsimonious machine learning method.

Methods

We included individuals who participated in an annual health checkup in Tokyo, Japan, between January 2008 and December 2018. In the training set (randomly selected 80% of the sample), we developed a novel interpretable model, Steatotic Liver Index (SLI), using a modified method of least absolute shrinkage and selection operator regression focusing on parsimony and interpretability using as few variables as FLI, and confirming its superiority to FLI using the test set (the remaining 20%). The predictive performance of the constructed index was assessed for the diagnosis, development, and remission of SLD.

Results

Among ultrasound data of 92 968 participants at the first health checkup, 20 380 (21.9%) had SLD. Using a modified method of least absolute shrinkage and selection operator regression, SLI was constructed with four variables: body mass index, waist circumference, alanine aminotransferase, and triglycerides. The C-statistic of SLI for SLD diagnosis was superior to that of FLI (0.909 vs. 0.892, p < 0.001). In participants without SLD, SLI was more accurate than FLI in predicting SLD development, whereas among those with SLD, SLI showed better accuracy in predicting SLD remission compared with FLI.

Conclusions

We developed SLI, a novel interpretable and parsimonious index for diagnosing SLD, which demonstrates superior predictive capability compared with FLI. Further studies are necessary to validate the diagnostic ability outside Japan.