P0992 - The Visualization of the Importance of Covariates in a Machine Learning Model for Advanced Liver Fibrosis

Sunday, October 22, 2023

3:30 PM – 7:00 PM PT

Location: Exhibit Hall

Has Audio

Presenting Author(s)

Samuel Y. Huang, MD

ISMMS Mt. Sinai South Nassau

Samuel Huang, MD¹, Alexander Huang, BS²
¹ISMMS Mt. Sinai South Nassau, Oceanside, NY; ²Northwestern University Feinberg School of Medicine, Chicago, IL

Introduction: Accurate prediction of liver disease is vital for early intervention, given its potential severity. This study aims to improve the prediction of advanced liver fibrosis and investigate its associations with factors, ultimately contributing to healthier lifestyle choices and timely management of liver disease.

Methods: This cross-sectional study included adults from the US National Health and Nutrition Examination Survey (2017-2020). Questionnaires captured demographic, dietary, exercise, and mental health information. Advanced fibrosis was defined using liver stiffness measurement (LSM) with a 9.5 kPa threshold. XGBoost, a machine learning model, predicted fibrosis, assessed using AUROC. SHAP provided visual explanations of the model's predictions and feature contributions. Model gain, cover, and frequency measured feature importance, enabling transparent and interpretable analysis.

Results: The results of the study revealed a high area under the receiver operating curve (AUROC) of 0.88, indicating the strong predictive performance of the XGBoost machine learning model for advanced liver fibrosis. The top co-variates that influenced the model's predictions, as identified by the shapely additive explanations (SHAP), included waist circumference, gamma glutamyl transferase, age, platelet count, aspartate aminotransferase, BMI, total serum cholesterol, hip circumference, and blood glucose. These variables demonstrated significant associations with the likelihood of advanced liver fibrosis.

Furthermore, when considering the relative contribution of each feature to the model, as measured by gain, the top four co-variates were waist circumference, gamma glutamyl transferase, platelet count, and aspartate aminotransferase levels. These factors played a crucial role in determining the predictions made by the XGBoost model.

Discussion: Machine learning with visualization algorithms provide interpretable explanations for the reader. The cross-sectional nature of this study limits the ability to draw definitive conclusions about causality or temporal relationships between variables. While the findings provide valuable insights into the associations between covariates and advanced liver fibrosis, further research is needed to establish the directionality of these relationships and confirm the generalizability of the results.

Figure: Overall SHAP Explanations: SHAP explanations use a color scheme where the purple color represents higher values of a specific covariate, while yellow represents lower values of the same covariate. On the x-axis, we observe the change in log-odds associated with advanced liver fibrosis

Feature	Gain	Cover	Frequency
BMXWAIST - Waist Circumference (cm)	0.185	0.139	0.068
Gamma Glutamyl Transferase (GGT) (IU/L)	0.101	0.086	0.056
Platelet count (1000 cells/uL)	0.059	0.067	0.054
Aspartate Aminotransferase (AST) (U/L)	0.057	0.055	0.044
BMXWT - Weight (kg)	0.049	0.033	0.033
Direct HDL-Cholesterol (mmol/L)	0.039	0.054	0.074
Ferritin (ng/mL)	0.034	0.032	0.034
Total Cholesterol (mg/dL)	0.032	0.031	0.053
BMXHIP - Hip Circumference (cm)	0.029	0.061	0.037
HS C-Reactive Protein (mg/L)	0.025	0.064	0.039
Fasting Glucose (mg/dL)	0.025	0.043	0.031
BMXBMI - Body Mass Index (kg/m**2)	0.025	0.040	0.026
Total Bilirubin (umol/L)	0.025	0.004	0.020
Age	0.024	0.035	0.028
Lymphocyte percent (%)	0.023	0.017	0.042
Globulin (g/dL)	0.020	0.031	0.026
Lactate Dehydrogenase (LDH) (IU/L)	0.019	0.004	0.021
BMXARMC - Arm Circumference (cm)	0.017	0.025	0.024
Blood Urea Nitrogen (mg/dL)	0.017	0.018	0.020
BMXHT - Standing Height (cm)	0.016	0.006	0.020
Uric acid (mg/dL)	0.016	0.017	0.019
Insulin (ŒºU/mL)	0.016	0.034	0.025
BMXARML - Upper Arm Length (cm)	0.015	0.006	0.020
Cholesterol, refrigerated serum (mg/dL)	0.014	0.020	0.016
Triglycerides, refrig serum (mg/dL)	0.013	0.007	0.018
Monocyte percent (%)	0.013	0.011	0.024
Creatinine, refrigerated serum (mg/dL)	0.013	0.006	0.016
Red cell distribution width (%)	0.012	0.016	0.022
Albumin, refrigerated serum (g/dL)	0.011	0.005	0.016
Mean platelet volume (fL)	0.009	0.006	0.017
Monocyte number (1000 cells/uL)	0.006	0.008	0.011
MCQ160b - Ever told had congestive heart failure	0.004	0.001	0.003
weight_loss_10lbs	0.003	0.000	0.004
MCQ366b - Doctor told you to exercise	0.003	0.005	0.003
MCQ300c - Close relative had diabetes?	0.003	0.001	0.002
Days.vigorous.recreational.activities	0.003	0.000	0.003
MCQ560 - Ever had gallbladder surgery?	0.002	0.007	0.004
Height_inches	0.002	0.001	0.002
General Health Condition	0.002	0.001	0.003
General Health	0.002	0.000	0.002
Ever told doctor had trouble sleeping?	0.002	0.000	0.002
MCQ371a - Are you now controlling or losing weight	0.002	0.000	0.002
Female Gender	0.002	0.000	0.001
MCQ366a - Doctor told you to control/lose weight	0.001	0.000	0.002
MCQ160a - Doctor ever said you had arthritis	0.001	0.000	0.002
How do you consider your weight?	0.001	0.000	0.002
MCQ160e - Ever told you had heart attack	0.001	0.001	0.001
like_to_weigh	0.001	0.000	0.001
MCQ366d - Doctor told you to reduce fat/calories	0.001	0.001	0.001
MCQ550 - Has DR ever said you have gallstones	0.001	0.000	0.001
MCQ080 - Doctor ever said you were overweight	0.001	0.001	0.001
MCQ366c - Doctor told you to reduce salt in diet	0.001	0.000	0.001
MCQ371c - Are you now reducing salt in diet	0.000	0.000	0.001
MCQ160c - Ever told you had coronary heart disease	0.000	0.000	0.000
BMILEG	0.000	0.001	0.000

Table: The Gain, Cover, and Frequency of all covariates within the XGBoost model. The Gain represents the relative contribution of the feature to the model and is the most important metric of model importance within this study. Covariates ordered according to the Gain statistic.

Disclosures:

Samuel Huang indicated no relevant financial relationships.

Alexander Huang indicated no relevant financial relationships.

Samuel Huang, MD¹, Alexander Huang, BS². P0992 - The Visualization of the Importance of Covariates in a Machine Learning Model for Advanced Liver Fibrosis, ACG 2023 Annual Scientific Meeting Abstracts. Vancouver, BC, Canada: American College of Gastroenterology.