Machine Learning Approach for Predicting Appointment No-Show in Healthcare
A Master of Science thesis in Engineering Systems Management by Dana Khalouf entitled, “A Machine Learning Approach for Predicting Appointment No-Show in Healthcare”, submitted in July 2024. Thesis advisor is Dr. Abdulrahim Shamayleh and thesis co-advisors are Dr. Hussam Alshariedeh and Dr. Mahmoud...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| التنسيق: | doctoralThesis |
| منشور في: |
2024
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://hdl.handle.net/11073/25716 |
| الوسوم: |
إضافة وسم
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| _version_ | 1864513432878841856 |
|---|---|
| author | Khalouf, Dana |
| author_facet | Khalouf, Dana |
| author_role | author |
| dc.contributor.none.fl_str_mv | Shamayleh, Abdulrahim Alshariedeh, Hussam Awad, Mahmoud |
| dc.creator.none.fl_str_mv | Khalouf, Dana |
| dc.date.none.fl_str_mv | 2024-11-19T05:38:52Z 2024-11-19T05:38:52Z 2024-07 |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | 35.232-2024.42 https://hdl.handle.net/11073/25716 |
| dc.language.none.fl_str_mv | en_US |
| dc.subject.none.fl_str_mv | Patient no-show Missed appointment Outpatient Machine learning Healthcare |
| dc.title.none.fl_str_mv | Machine Learning Approach for Predicting Appointment No-Show in Healthcare |
| dc.type.none.fl_str_mv | info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/doctoralThesis |
| description | A Master of Science thesis in Engineering Systems Management by Dana Khalouf entitled, “A Machine Learning Approach for Predicting Appointment No-Show in Healthcare”, submitted in July 2024. Thesis advisor is Dr. Abdulrahim Shamayleh and thesis co-advisors are Dr. Hussam Alshariedeh and Dr. Mahmoud Awad. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form). |
| format | doctoralThesis |
| id | aus_2517b3bbb018c49b2526f78f4056e85a |
| identifier_str_mv | 35.232-2024.42 |
| language_invalid_str_mv | en_US |
| network_acronym_str | aus |
| network_name_str | aus |
| oai_identifier_str | oai:repository.aus.edu:11073/25716 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Machine Learning Approach for Predicting Appointment No-Show in HealthcareKhalouf, DanaPatient no-showMissed appointmentOutpatientMachine learningHealthcareA Master of Science thesis in Engineering Systems Management by Dana Khalouf entitled, “A Machine Learning Approach for Predicting Appointment No-Show in Healthcare”, submitted in July 2024. Thesis advisor is Dr. Abdulrahim Shamayleh and thesis co-advisors are Dr. Hussam Alshariedeh and Dr. Mahmoud Awad. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).The efficiency of public healthcare delivery is essential to achieving optimal health outcomes for patients. One factor that hinders efficiency is patient no-shows, which should be managed effectively to reduce its adverse impacts on patients and healthcare providers socially and economically. In particular, it reduces patient care access, underutilizes resources, and leads to lost revenue. A no-show, or missed appointment, happens when the outpatient does not attend the scheduled appointment or cancels it at short notice. It is a common challenge faced by several healthcare systems. Previous studies have considered different models to identify patients more likely to miss their appointments; however, no study analyzed appointment no-shows in the United Arab Emirates. Therefore, this study used a data analytics and machine learning approach to develop a classification model to predict whether an outpatient will miss their appointment in Dubai's primary healthcare clinics. While data analysis is applied to extract insights from historical data and identify the most useful features, machine learning tools extrapolate on historical data to generate future predictions. A historcial dataset of appointments for the period 2021-2022 is utilized in this study. A prediction accuracy of 78% and an AUC of 0.859 were achieved using Gradient Boosted Trees while optimizing on Youden’s Index. In addition, the most influential drivers of patient no-shows were identified from the feature importances produced from the tuned model and an extensive exploratory data analysis, which included the patient’s health plan, the clinic, and the patient’s weight. As a result, recommendations of startgeies were proposed to DHA clinics to reduce no-shows, which will improve efficiency and enhance patient access to care.College of EngineeringDepartment of Industrial EngineeringMaster of Science in Engineering Systems Management (MSESM)Shamayleh, AbdulrahimAlshariedeh, HussamAwad, Mahmoud2024-11-19T05:38:52Z2024-11-19T05:38:52Z2024-07info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisapplication/pdf35.232-2024.42https://hdl.handle.net/11073/25716en_USoai:repository.aus.edu:11073/257162025-06-26T12:22:30Z |
| spellingShingle | Machine Learning Approach for Predicting Appointment No-Show in Healthcare Khalouf, Dana Patient no-show Missed appointment Outpatient Machine learning Healthcare |
| status_str | publishedVersion |
| title | Machine Learning Approach for Predicting Appointment No-Show in Healthcare |
| title_full | Machine Learning Approach for Predicting Appointment No-Show in Healthcare |
| title_fullStr | Machine Learning Approach for Predicting Appointment No-Show in Healthcare |
| title_full_unstemmed | Machine Learning Approach for Predicting Appointment No-Show in Healthcare |
| title_short | Machine Learning Approach for Predicting Appointment No-Show in Healthcare |
| title_sort | Machine Learning Approach for Predicting Appointment No-Show in Healthcare |
| topic | Patient no-show Missed appointment Outpatient Machine learning Healthcare |
| url | https://hdl.handle.net/11073/25716 |