BMC psychiatry · 2025
Utilizing machine learning algorithms for predicting Anxiety-Depression Comorbidity Syndrome in Gastroenterology Inpatients (ADCS-GI).
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Tan Min, Zhao Jinjin, Tao Yushun, Sehar Uroosa, Yan Yan, Zou Qian, Liu Qing, Xu Long, Xia Zeyang, Feng Lijuan, Xiong Jing
Аннотация
BACKGROUND: Accurately diagnosing Anxiety-Depression Comorbidity Syndrome in Gastroenterology Inpatients (ADCS-GI) shows significant challenges as traditional diagnostic methods fail to meet expectations due to patient hesitance and non-psychiatric healthcare professionals' limitations. Therefore, the need for objective diagnostics highlights the potential of machine learning in identifying and treating ADCS-GI.
METHODS: A total of 1186 ADCS patients were recruited for this study. We conducted extensive studies for the dataset, including data quantification, equilibrium, and correlation analysis. Eight machine learning models, including Gaussian Naive Bayes (NB), Support Vector Classifier (SVC), K-Neighbors Classifier, RandomForest, XGB, CatBoost, Cascade Forest, and Decision Tree, were utilized to compare prediction efficacy, with an effort to minimize the dependency on subjective questionnaires.
RESULTS: Among eight machine learning algorithms, the Decision Tree and K-nearest neighbors models demonstrated an accuracy exceeding 81% and a sensitivity in the same range for detecting ADCS in patients. Notably, when identifying moderate and severe cases, the models achieved an accuracy above 88% and a sensitivity of 90%. Furthermore, the models trained without reliance on subjective questionnaires showed promising performance, indicating the feasibility of developing questionnaire-free early detection applications.
CONCLUSION: Machine learning algorithms can be used to identify ADCS among gastroenterology patients. This can help facilitate the early detection and intervention of psychological disorders in gastroenterology patients' care.
METHODS: A total of 1186 ADCS patients were recruited for this study. We conducted extensive studies for the dataset, including data quantification, equilibrium, and correlation analysis. Eight machine learning models, including Gaussian Naive Bayes (NB), Support Vector Classifier (SVC), K-Neighbors Classifier, RandomForest, XGB, CatBoost, Cascade Forest, and Decision Tree, were utilized to compare prediction efficacy, with an effort to minimize the dependency on subjective questionnaires.
RESULTS: Among eight machine learning algorithms, the Decision Tree and K-nearest neighbors models demonstrated an accuracy exceeding 81% and a sensitivity in the same range for detecting ADCS in patients. Notably, when identifying moderate and severe cases, the models achieved an accuracy above 88% and a sensitivity of 90%. Furthermore, the models trained without reliance on subjective questionnaires showed promising performance, indicating the feasibility of developing questionnaire-free early detection applications.
CONCLUSION: Machine learning algorithms can be used to identify ADCS among gastroenterology patients. This can help facilitate the early detection and intervention of psychological disorders in gastroenterology patients' care.