Machine Learning-Based Prediction of Helicobacter pylori Infection Study in Adults

Abstract

BACKGROUND Helicobacter pylori has a high infection rate worldwide, and epidemiological study of H. pylori is important. Artificial intelligence has been widely used in the field of medical research and has become a hotspot in recent years. This paper proposed a prediction model for H. pylori infection based on machine learning in adults. MATERIAL AND METHODS Adult patients were selected as research participants, and information on 30 factors was collected. The chi-square test, mutual information, ReliefF, and information gain were used to screen the feature factors and establish 2 subsets. We constructed an H. pylori infection prediction model based on XGBoost and optimized the model using a grid search by analyzing the correlation between features. The performance of the model was assessed by comparing its accuracy, recall, precision, F1 score, and AUC with those of 4 other classical machine learning methods. RESULTS The model performed better on the part B subset than on the part A subset. Compared with the other 4 machine learning methods, the model had the highest accuracy, recall, F1 score, and AUC. SHAP was used to evaluate the importance of features in the model. It was found that H. pylori infection of family members, living in rural areas, poor washing hands before meals and after using the toilet were risk factors for H. pylori infection. CONCLUSIONS The model proposed in this paper is superior to other models in predicting H. pylori infection and can provide a scientific basis for identifying the population susceptible to H. pylori and preventing H. pylori infection.

Figure 1

Patient inclusion and model training process.

Figure 2

Feature assignment importance ranking by heatmap. Part A: the sum of the importance of the 4 algorithms ranked the top 10 features. Part B: The sum of the importance of the 4 algorithms ranked the top 15 features.

Figure 3

AUC curve of the prediction model: (A) part A; (B) part B.

Figure 4

Feature importance histogram.

Figure 5

Feature importance scatter plot. SHAP value: negative SHAP values indicate a decreased risk of infection, positive SHAP values indicate an increased risk of infection, and the color represents the magnitude of the feature value.

Figure 6

Violin plots of feature importance for living area, hand-washing before meals and after using the toilet, and sharing utensils (SHAP values >0: danger; SHAP values <0: protection).