Integrating machine learning algorithms and explainable artificial intelligence approach for predicting patient unpunctuality in psychiatric clinics

Abstract

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 efficient scheduling. These models are analyzed using the explainable artificial intelligence approach and the Shapley additive explanations model, promoting comprehension and trust in our algorithmic results. Using three years of appointment data from a psychiatric clinic, we identify the travel distance, appointment lead time, patient’s age, Body Mass Index (BMI), and certain mental diagnoses as significant factors affecting the patient’s unpunctuality. Despite the good predictive potential of machine learning algorithms, no single model excels in all performance metrics. The study proposes implementing these machine learning techniques and the explainable artificial intelligence tool into the clinic’s appointment system as a decision support system to minimize patient unpunctuality.