Abstract—Introduction: Patient no-shows are defined as patients who missed outpatient appointments, either for diagnostic or clinic tests. Identifying those patients is necessary for clinicians and healthcare settings to utilize the resources and improve healthcare efficiency appropriately. This research paper aims to develop a predictive model based on machine learning algorithms to predict patients' failure to attend scheduled appointments. A public data set was divided into training and testing data sets. Two machine learning algorithms, namely decision trees and AdaBoost, were evaluated based on Precision, Recall, True Positive Rate, False Negative Rate, F-measure, and Receiver Operating Characteristic (ROC). Results showed that the decision tree outperformed AdaBoost. The most significant predictors were age and lead time.
 

Index Terms—no-show, decision tree, AdaBoost, machine learning

Cite: Abdulwahhab Alshammari, Raed Almalki, and Riyad Alshammari, "Developing a Predictive Model of Predicting Appointment No-Show by Using Machine Learning Algorithms," Journal of Advances in Information Technology, Vol. 12, No. 3, pp. 234-239, August 2021. doi: 10.12720/jait.12.3.234-239

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