The Prediction of Pediatric Outpatient No-Show Visits by Employing Machine Learning Framework

Abstract

Patient no-show for a booked medical appointment is a significant problem that negatively impacts healthcare resource utilization, cost, efficiency, quality, and patient outcomes. This paper developed a machine learning framework to predict pediatric patients' no-shows to medical appointments accurately. Thirty months of outpatient visits data were extracted from data warehouse from January 2017 to July 2019 of the Ministry of National Guard Health Affairs (MNGHA), Saudi Arabia. The researchers retrieved the data from all healthcare facilities in the central region, and more than 100 attributes were generated. The data includes over 100,000 pediatric patients and more than 3.7 million visits. Five machine learning algorithms were deployed, where Gradient Boosting (GB) algorithm outperformed the other four machine learning algorithms: decision tree, random forest, logistic regression, and neural network. The study evaluated and compared the performance of the five models based on five evaluations criteria. GB achieved a Receiver Operating Characteristic (ROC) score of 97.1%. Furthermore, this research paper identified the factors that have massive potential for effecting patients' adherence to scheduled appointments.