Prediction of Outpatient No-Show Appointments Using Machine Learning Algorithms for Pediatric Patients in Saudi Arabia

Patient no-shows are prevalent in pediatric outpatient visits, leading to underutilized medical resources, increased healthcare costs, reduced clinic efficiency, and decreased access to care. The use of machine learning techniques provides insights to mitigate this problem. This study aimed to develop a predictive model for patient no-shows at the Ministry of National Guard Health-Affairs, Saudi Arabia, and evaluate the results of various machine learning algorithms in predicting these events. Four machine learning algorithms - Gradient Boosting, AdaBoost, Random Forest, and Naive Bayes - were used to create predictive models for patient no-shows. Each model underwent extensive parameter tuning and reliability assessment to ensure robust performance, including sensitivity analysis and cross-validation. Gradient Boosting achieved the highest area under the receiver operating curve (AUC) of 0.902 and Classification Accuracy (CA) of 0.944, while the AdaBoost model achieved an AUC of 0.812 and CA of 0.927. The Naive Bayes and Random Forest models achieved AUCs of 0.677 and 0.889 and CAs of 0.915 and 0.937, respectively. The confusion matrix demonstrated high true-positive rates for no-shows for the Gradient Boosting and Random Forest models, while Naive Bayes had the lowest values. The Gradient Boosting and Random Forest models were most effective in predicting patient no-shows. These models could enhance outpatient clinic efficiency by predicting no-shows. Future research can further refine these models and investigate practical strategies for their implementation.

@article{Alshammari2024,
  title     = {Prediction of Outpatient No-Show Appointments Using Machine Learning Algorithms for Pediatric Patients in Saudi Arabia},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {8},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Abdulwahhab Alshammari and Fahad Alotaibi and Sana Alnafrani},
  doi       = {10.14569/IJACSA.2024.0150812},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150812}
}