(untitled) (The Science Post, 2025)
Patient no-shows for scheduled appointments represent a critical operational and financial challenge for healthcare providers, leading to wasted resources, reduced access to care, and increased wait times. This paper investigates the application of machine learning (ML) to predict the risk of a patient missing their appointment. By analyzing historical electronic health record (EHR) and scheduling data, predictive models can identify patients with a high probability of no-show, enabling clinics to implement targeted interventions such as reminder calls, overbooking strategies, or offering slots to waitlisted patients. This research details the development of several classification algorithms, including Logistic Regression, Random Forest, and Gradient Boosting, to forecast no-show events. The results demonstrate that ensemble methods can accurately identify high-risk patients, providing a data-driven foundation for improving clinic efficiency, maximizing resource utilization, and enhancing patient access to care