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International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 10, 2020.
Abstract: Patients’ no-show is one of the leading causes of increasing financial burden for healthcare organizations and is an indicator of healthcare systems' quality and performance. Patients' no-show affects healthcare delivery, workflow, and resource planning. The study aims to develop a prediction model predict no-show visits using a machine learning approach. A large volume of data was extracted from electronic health records of patient visits in outpatient clinics under the umbrella of large medical cities in Saudi Arabia. The data consists of more than 33 million visits, with an 85% no-show rate. A total of 29 features were utilized based on demographic, clinical, and appointment characteristics. Nine features were an original data element, while data elements derived 20 features. This study used and compared three machine learning algorithms; Deep Neural Network (DNN), AdaBoost, and Naive Bayes (NB). Results revealed that the DNN performed better in comparison to NB and AdaBoost. DNN achieved a weighted average of 98.2% and 94.3% of precision and recall, respectively. This study shows that machine learning has the potential to improve the efficiency and effectiveness of healthcare. The results are considered promising, and the model can be an excellent candidate for implementation.
Riyad Alshammari, Tahani Daghistani and Abdulwahhab Alshammari, “The Prediction of Outpatient No-Show Visits by using Deep Neural Network from Large Data” International Journal of Advanced Computer Science and Applications(IJACSA), 11(10), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0111066
@article{Alshammari2020,
title = {The Prediction of Outpatient No-Show Visits by using Deep Neural Network from Large Data},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0111066},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0111066},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {10},
author = {Riyad Alshammari and Tahani Daghistani and Abdulwahhab Alshammari}
}
Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.