Integrating machine learning algorithms and explainable artificial intelligence approach for predicting patient unpunctuality in psychiatric clinics
This study addresses patient unpunctuality, a major concern affecting patient waiting time, resource utilization, and quality of care.We develop and compare four machine learning models, including multinomial logistic regression, decision tree, random forest, and artificial neural network, to accurately predict patient arrival patterns and aid effi