Investigating Key Contributors to Hospital Appointment No-Shows Using Explainable AI
- Title
- Investigating Key Contributors to Hospital Appointment No-Shows Using Explainable AI
- Creator
- Yiye V.; Ugbomeh O.; Ezenkwu C.P.; Ibeke E.; Sharma V.; Alkhayyat A.
- Description
- The healthcare sector has suffered from wastage of resources and poor service delivery due to the significant impact of appointment no-shows. To address this issue, this paper uses explainable artificial intelligence (XAI) to identify major predictors of no-show behaviours among patients. Six machine learning models were developed and evaluated on this task using Area Under the Precision-Recall Curve (AUC-PR) and F1-score as metrics. Our experiment demonstrates that Support Vector Classifier and Multilayer Perceptron perform the best, with both scoring the same AUC-PR of 0.56, but different F1-scores of 0.91 and 0.92, respectively. We analysed the interpretability of the models using Local Interpretable Model-agnostic Explanation (LIME) and SHapley Additive exPlanations (SHAP). The outcome of the analyses demonstrates that predictors such as the patients' history of missed appointments, the waiting time from scheduling time to the appointments, patients' age, and existing medical conditions such as diabetes and hypertension are essential flags for no-show behaviours. Following the insights gained from the analyses, this paper recommends interventions for addressing the issue of medical appointment no-shows. 2024 IEEE.
- Source
- 2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024
- Date
- 2024-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- AI; Artificial Intelligence; Explainable; Health Analytics; Health Informatics; Healthcare; Hospital; Interpretable; LIME; Machine Learning; No-Shows; SHAP; XAI
- Coverage
- Yiye V., Robert Gordon University, School of Creative and Cultural Business, Aberdeen, United Kingdom; Ugbomeh O., Robert Gordon University, School of Creative and Cultural Business, Aberdeen, United Kingdom; Ezenkwu C.P., Robert Gordon University, School of Computing, Engrg.,and Tech, Aberdeen, United Kingdom; Ibeke E., Robert Gordon University, School of Computing, Engrg.,and Tech, Aberdeen, United Kingdom; Sharma V., Christ University, Computer Science Department, India; Alkhayyat A., Islamic University, College of Technical Engineering, Najaf, Iraq
- Rights
- Restricted Access
- Relation
- ISBN: 979-835037809-2
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Yiye V.; Ugbomeh O.; Ezenkwu C.P.; Ibeke E.; Sharma V.; Alkhayyat A., “Investigating Key Contributors to Hospital Appointment No-Shows Using Explainable AI,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 23, 2025, https://archives.christuniversity.in/items/show/19073.