Applying machine learning to predict bowel preparation adequacy in elderly patients for colonoscopy: development and validation of a web-based prediction tool

Adequate bowel preparation is crucial for effective colonoscopy, especially in elderly patients who face a high risk of inadequate preparation. This study develops and validates a machine learning model to predict bowel preparation adequacy in elderly patients before colonoscopy. The study adhered to the TRIPOD AI guidelines. Clinical data from 471 elderly patients collected between February and December 2023 were utilized for developing and internally validating the model, while 221 patients’ data from March to June 2024 were used for external validation. The Boruta algorithm was applied for feature selection. Models including logistic regression, light gradient boosting machines, support vector machines (SVM), decision trees, random forests, and extreme gradient boosting were evaluated using metrics such as AUC, accuracy, sensitivity, and specificity. The SHAP algorithm helped rank feature importance. A web-based application was developed using the Streamlit framework to enhance clinical usability. The Boruta algorithm identified 7 key features. The SVM model excelled with an AUC of 0.895 (95% CI: 0.822–0.969), and high accuracy, sensitivity, and specificity. In external validation, the SVM model maintained robust performance with an AUC of 0.889. The SHAP algorithm further explained the contribution of each feature to model predictions. The study developed an interpretable and practical machine learning model for predicting bowel preparation adequacy in elderly patients, facilitating early interventions to improve outcomes and reduce resource wastage. This study developed a machine learning model to predict bowel preparation adequacy in elderly patients undergoing colonoscopy, notably improving prediction accuracy and aiding clinical decision-making.Multiple machine learning models were used to predict bowel preparation adequacy, with the support vector machine (SVM) achieving the best performance. SHAP analysis enhanced the interpretability of the model by identifying key predictive factors, making it a reliable and transparent tool for clinical use.The predictive model was integrated into a user-friendly web application, enabling healthcare providers to identify high-risk patients early and enhance the quality of bowel preparation interventions. This study developed a machine learning model to predict bowel preparation adequacy in elderly patients undergoing colonoscopy, notably improving prediction accuracy and aiding clinical decision-making. Multiple machine learning models were used to predict bowel preparation adequacy, with the support vector machine (SVM) achieving the best performance. SHAP analysis enhanced the interpretability of the model by identifying key predictive factors, making it a reliable and transparent tool for clinical use. The predictive model was integrated into a user-friendly web application, enabling healthcare providers to identify high-risk patients early and enhance the quality of bowel preparation interventions.