An Advanced Machine Learning Framework to Identify Associated Factors and Predict the Risk of Oral Candidiasis in Cancer Patients - Bach , Duy Thai; Truong , Tuan Khanh; Bui , Anh Thu; Trieu , Thi Oanh; Bui , Tuan Dat; Nguyen , Thi Hong Chuyen; Ton , Nu Phuong Anh (Tạp chí Y Dược Huế, 2025)
Background: Oral candidiasis is a common opportunistic infection in cancer patients, particularly those undergoing chemotherapy. Multiple clinical and hematological factors contribute to infection risk, but their complex interactions remain poorly understood using conventional statistical methods. Objectives: To identify associated factors, and develop machine learning models to predict infection risk of oral candidiasis among cancer patients receiving and not yet receiving chemotherapy. Materials and Methods: This cross-sectional study enrolled 69 cancer patients at Hue University of Medicine and Pharmacy Hospital between October 2024 and May 2025. Patients underwent clinical examinations, laboratory testing, direct oral swab microscopy and cultivation for candidiasis diagnosis. Sparse Partial Least Squares Discriminant Analysis (sPLS-DA) was used to select relevant features. eXtreme Gradient Boosting (XGBoost) models were developed for each patient group (chemotherapy and non-chemotherapy) and interpreted using SHapley Additive exPlanations (SHAP) value method. Results: Oral candidiasis was detected in 36.8% of chemotherapy patients and 35.4% of non-chemotherapy patients. Key associated factors included dry mouth, taste change, white patches on mucosa, low lymphocyte or red blood cell counts, poor oral hygiene, and antibiotic use. XGBoost models achieved high performance in both groups (AUC-ROC: 0.9093 for chemotherapy; 0.8758 for non-chemotherapy). SHAP analysis revealed feature-specific contributions aligned with clinical relevance, confirming the model’s interpretability and consistency. Conclusion: Oral candidiasis is highly prevalent among cancer patients, with distinct risk profiles between those with and without chemotherapy. Machine learning methods such as sPLS-DA and XGBoost effectively identified and interpreted predictive factors, offering valuable tools for clinical risk stratification and early prevention in oncology care.