Machine Learning-Based Patient No-Show Prediction for Hospital Appointment Systems
DOI:
https://doi.org/10.59890/ijels.v4i6.23Keywords:
Patient No-Show, Machine Learning, Decision Tree, Hospital Information System, Healthcare AnalyticsAbstract
Patient no-show is a major challenge in hospital appointment systems because it affects operational efficiency, physician utilization, and healthcare service quality. This study aims to implement a machine learning approach for predicting patient no-show in Hospital Management Information Systems (HMIS) using the Decision Tree algorithm. The dataset used in this study consisted of 200 hospital appointment records containing appointment schedules, physician information, visit reasons, and appointment status. The research methodology included exploratory data analysis, data preprocessing, feature engineering, categorical data encoding, and machine learning modeling. The analysis showed that no-show appointments represented a considerable portion of the dataset, with higher no-show frequencies occurring on certain appointment days and sessions. The Decision Tree model was trained using appointment-related features, including doctor_id, reason_for_visit, day_name, and session. The evaluation results demonstrated that the model achieved an accuracy of 60% with acceptable recall performance for detecting no-show patients. The findings indicate that machine learning has the potential to support predictive analytics and operational decision-making in hospital appointment systems.
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