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Study population

The study population consisted of patients diagnosed with Schistosoma japonicum in Yueyang, Hunan Province, China. This city has historically been a high schistosomiasis epidemic area. Because it was located near Dongting Lake in the middle and lower reaches of the Yangtze River, where the Intermediate host Oncomelania hupensis breeds in large numbers.

Schistosoma japonicum infection was diagnosed according to the definition of Zhou et al.26. Including the following diagnostic criteria: life history in schistosomiasis-endemic areas, contact with infected water, specific schistosoma serology testing, color ultrasound, excreta (feces, urine) microscopic examination. Schistosomiasis infection was considered when schistosome ova were visualized in stool, urine or when the Schistosoma serology was positive.

Liver fibrosis was determined by ultrasound according to the World Health Organization diagnostic criteria for Schistosoma japonicum infection27,28. An experienced ultrasound expert divided the patients into two groups according to the ultrasound results: fibrosis group (with mesh-like changes and uneven hepatic echotexture); no-fibrosis group (without mesh-like changes, smooth and uniform hepatic echotexture). The diagnosis was double-checked by another experienced schistosomiasis specialist.

A retrospective medical record review was conducted from June 2019 to June 2022 at Xiangyue Hospital, Yueyang City, Hunan Province of China. All patients underwent blood tests and ultrasound evaluation at admission. All variables were extracted from the hospitals electronic medical record system. The data include: patient demographic characteristics, blood routine indicators and other variables. KNN filling method is used to fill in the missing data. The principle is to identify k samples that are spatially similar or close in the data set through distance measurement, and then use these k samples to estimate the value of the missing data point. The percentage of missing data points is presented in Supplementary Table 5. The LassoCV method was used to screen out key variables. Data entry was performed by a full-time research physician or medical student. This study was conducted and approved by the Ethics Committee of the third Xiangya Hospital of Central South University (No: 21149) and has been carried out in accordance with the Code of Ethics of the World Medical Association (Declaration of Helsinki) for experiments. All methods were performed in accordance with the relevant guidelines and regulations. The need of informed consent was waived by the Ethics Committee of the third Xiangya Hospital of Central South University due to retrospective nature of the study. The privacy of all participants is fully protected.

Patients were divided into hepatic fibrosis and non-hepatic fibrosis groups according to their color Doppler ultrasound results. Patients with hepatitis B virus (hepatitis B surface antigen seropositive), hepatitis C virus (HCV antibody seropositive), human immunodeficiency virus (HIV antibody seropositive), alcoholic and non-alcoholic fatty liver disease (ultrasound scanning and alcohol consumption above 30g daily), decompensated liver disease or liver cancer (ultrasound and liver function tests), and organ transplantation (self-reported) were excluded. The key variables are selected by LassoCV method for subsequent modeling.

First, the classification task was completed using 6 machine learning algorithms, including: XGB Classifier, Logistic Regression, LightGBM Classifier, Random Forest Classifier, Support Vector Classification, K Neighbors Classifier. Fivefold cross-validation method was used for validation. Each model was evaluated using AUC, clinical decision curve plot, accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score. The ROC diagram and the forest diagram show the ROC results of each model for the prediction of hepatic fibrosis.

After selecting the best algorithm through multi-algorithm model comparison, the best algorithm was used to model again. Different from multi-model comparison, when using the best-performing algorithm for modeling, we randomly select 15% of the total samples as the test set, and the remaining samples are used as the training set for fivefold cross-validation.

The SHAP package in python can interpret the output of machine learning models, considering all features as contributors. For each prediction sample, the model will generate a prediction value, and its biggest advantage is that it can reflect the influence of the characteristics in each sample and show the positive and negative effects. This study used the SHAP package to interpret the model. SHAP value plots were used to show the contribution of each variable in the model. Model variable importance plots were used to show the importance ranking of each variable. Force diagrams were used to illustrate how each variable affects the predicted outcome for each sample with two examples.

The python used in this study is version 3.7. The statsmodels 0.11.1 package in Python was used to count whether each variable was different between two groups of people. The analysis method was selected according to the distribution of samples, homogeneity of variance, and sample size. Chi-square test was used for categorical variables. Students t-test or MannWhitney U-test was used for quantitative variables.

In this study, LassoCV was used to screen key variables, and factors with a coefficient of 0 were automatically eliminated (sklearn 0.22.1 package in Python). Lasso obtains a more refined model by constructing a penalty function, so that it compresses some regression coefficients, that is, forces the sum of the absolute values of the coefficients to be less than a certain fixed value; at the same time, sets some regression coefficients to zero. Therefore, the advantage of subset shrinkage is preserved, and it is a biased estimate for dealing with data with multicollinearity. In the multi-model and best-model modeling process, the xgboost 1.2.1 package of Python is used for XGBoost algorithm modeling, the lightgbm 3.2.1 package of Python is used for LightGBM algorithm modeling, and the sklearn 0.22.1 package of Python was used to build other models. The shap 0.39.0 package in python was used to demonstrate the interpretability of the model.

Ethics approval was obtained from the Ethics Committee of the third Xiangya Hospital of Central South University.

Link:
Using blood routine indicators to establish a machine learning model for predicting liver fibrosis in patients with ... - Nature.com