Development of a prediction model for peptic ulcer disease using the fusion of a feature selection strategy and ensemble algorithms.

BACKGROUND: Peptic ulcer disease is a prevalent gastric and duodenal disorder associated with H. pylori infection, which is related to numerous complications and diseases. Early prediction of this disease plays a significant role in reducing and eliminating its complications. So far, machine learning has shown promise for efficiently predicting health conditions in healthcare settings. So, this study aimed to establish a prediction model by getting assistance from machine learning and risk factors for peptic ulcer disease to predict this disease efficiently and achieve preventive purposes. METHODS: This retrospective study was conducted from January 2018 to December 2023 and included 1803 individuals with suspected peptic ulcer disease. We used selected ensemble algorithms, including XG-Boost, Random Forest, Ada-Boost, LightGBM, and Cat-Boost, to build prediction models and choose the best-performing ones. RESULTS: XG-Boost with an AU-ROC of 0.936 and a 95% CI of AU-ROC = [0.909–0.956] obtained more predictive strength than others. Factors of the history of H. pylori, NSAIDs, family history of gastric ulcer, smoking, and fruit intake were considered the top-ranked risk factors for peptic ulcer disease. CONCLUSIONS: This study revealed that XG-Boost, with satisfactory performance and clinical usability, can potentially identify high-risk individuals for peptic ulcer disease in clinical settings.