Dual role of tea consumption in gastrointestinal disease risks: analysis using a risk prediction model integrating interpretable machine learning and large language model

doi:10.12122/j.issn.1673-4254.2026.02.13

Abstract

Abstract:

Objective To explore the correlation of tea consumption with risks of gastrointestinal diseases using a risk prediction model integrating interpretable machine learning and a large language model. Methods A survey was conducted among the patients undergoing both gastroscopy and 13C-urea breath testing at Gastrointestinal Endoscopy Center of Anxi Hospital of Traditional Chinese Medicine. Univariate analysis was performed to determine the suitability of feature selection. The collected data were randomly divided into training and testing sets in a 7:3 ratio. Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGB), and Deep Neural Network (DNN) were applied to identify the best classifier for predicting high-risk gastrointestinal conditions. Bayesian optimization algorithm was used to obtain the optimal hyperparameter combinations for the 6 models. After Model fitting, the interpretability of the best models was analyzed using SHapley Additive exPlanations (SHAP). The DeepSeek-R1 base language model was fine-tuned with gastrointestinal disease dataset and Chinese medical online consultation data to obtain the final model. Results The study included 503 participants. All the selected features showed association with gastrointestinal diseases, but only age exhibited a significant linear correlation (β=0.023, SE=0.008, t=2.942, P=0.003). DNN model performed the best with a good accuracy (0.68), precision (0.68), recall rate (0.85), F1 Score (0.75), and AUC (0.74). The top 3 important features were age, DOB value, and smoking history. The large language model constructed provided recommendations consistent with those of professional physicians based on gastroscopy results. Conclusion DNN model is effective for predicting gastrointestinal disease risk and offers reliable support for clinical risk assessment and decision-making regarding endoscopy. Smoking cessation, moderate alcohol consumption, and reasonable tea intake may help prevent gastrointestinal diseases.

Key words: gastrointestinal diseases, helicobacter pylori, machine learning, risk prediction, large language model

Junyao CHEN, Zeyu CHEN, Zhaojie LIN, Menghao FANG, Chaoying SHEN, Qi XU, Xiaoyi ZHANG, Lu LU. Dual role of tea consumption in gastrointestinal disease risks: analysis using a risk prediction model integrating interpretable machine learning and large language model[J]. Journal of Southern Medical University, 2026, 46(2): 353-361.

Figures/Tables 11

References 28

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Features	Assignment
Duration of smoking	<3 years=1,3-5 years=3,5-7 years=5,7-10 years=7,>10 years=10
Duration of tea drinking	<3 years=1,3-5 years=3,5-7 years=5,7-10 years=7,>10 years=10
Frequency of tea drinking	Once a week=1,2-3 times a week=2,4-6 times a week=6,Once a day=7,Several times a day =10

Features	Assignment
Duration of smoking	<3 years=1,3-5 years=3,5-7 years=5,7-10 years=7,>10 years=10
Duration of tea drinking	<3 years=1,3-5 years=3,5-7 years=5,7-10 years=7,>10 years=10
Frequency of tea drinking	Once a week=1,2-3 times a week=2,4-6 times a week=6,Once a day=7,Several times a day =10

Disease name	Gastroscopy findings	Common symptoms	Medical recommendations
Gastric antral ulcer (Stage A2)	The gastric antral ulcer shows a thinner coating, with reduced hyperemia and edema in the surrounding mucosa, and clearer margins compared to stage A1.	Postprandial abdominal pain has somewhat subsided,but symptoms of indigestion persist.	Diet and schedule remain the same as in Stage A1; Continue medication therapy, with dosage adjustments as needed; Surgery is generally not required, but the condition should be closely monitored.
Gastric antral ulcer (Stage H1)	The gastric antral ulcer has shrunk and become shallower, with a thin coating. Inflammation of the surrounding mucosa has subsided, and regenerative epithelium is present.	Postprandial abdominal pain is significantly reduced, and symptoms of indigestion are improved.	Dietary intake may be appropriately diversified; maintain regular schedule; continue medication as prescribed, with adjustments based on condition; surgery is generally not required.
Gastric antral ulcer (Stage S1)	The gastric antral ulcer has healed, leaving a red scar.	No obvious symptoms, with occasional dull pain in the upper abdomen.	Maintain a regular diet and eating schedule; adhere to a consistent sleep routine; medication may be discontinued for observation, with periodic endoscopic follow-ups; surgery is generally not required.

Disease name	Gastroscopy findings	Common symptoms	Medical recommendations
Gastric antral ulcer (Stage A2)	The gastric antral ulcer shows a thinner coating, with reduced hyperemia and edema in the surrounding mucosa, and clearer margins compared to stage A1.	Postprandial abdominal pain has somewhat subsided,but symptoms of indigestion persist.	Diet and schedule remain the same as in Stage A1; Continue medication therapy, with dosage adjustments as needed; Surgery is generally not required, but the condition should be closely monitored.
Gastric antral ulcer (Stage H1)	The gastric antral ulcer has shrunk and become shallower, with a thin coating. Inflammation of the surrounding mucosa has subsided, and regenerative epithelium is present.	Postprandial abdominal pain is significantly reduced, and symptoms of indigestion are improved.	Dietary intake may be appropriately diversified; maintain regular schedule; continue medication as prescribed, with adjustments based on condition; surgery is generally not required.
Gastric antral ulcer (Stage S1)	The gastric antral ulcer has healed, leaving a red scar.	No obvious symptoms, with occasional dull pain in the upper abdomen.	Maintain a regular diet and eating schedule; adhere to a consistent sleep routine; medication may be discontinued for observation, with periodic endoscopic follow-ups; surgery is generally not required.

Model	Accuracy	Precision	Recall	F1 Score	Brier Score	AUC
SVM	0.67±0.046	0.68±0.058	0.80±0.081	0.74±0.047	0.23±0.011	0.68±0.048
KNN	0.60±0.046	0.64±0.030	0.69±0.083	0.66±0.053	0.25±0.020	0.66±0.041
LR	0.69±0.039	0.68±0.036	0.85±0.061	0.76±0.037	0.21±0.017	0.74±0.063
RF	0.58±0.033	0.61±0.036	0.73±0.037	0.66±0.027	0.25±0.012	0.63±0.030
XGB	0.58±0.030	0.63±0.016	0.68±0.077	0.65±0.044	0.24±0.016	0.63±0.039
DNN	0.68±0.047	0.68±0.038	0.85±0.087	0.75±0.052	0.21±0.016	0.74±0.053