Investigating key contributors to hospital appointment no-shows using explainable AI.

Yiye, Versse; Ugbomeh, Ogochukwu; Ezenkwu, Chinedu Pascal; Ibeke, Ebuka; Sharma, Vandana; Alkhayyat, Ahmed

Abstract

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.

Citation

YIYE, V., UGBOMEH, O., EZENKWU, C.P., IBEKE, E., SHARMA, V. and ALKHAYYAT, A. 2024. Investigating key contributors to hospital appointment no-shows using explainable AI. In Proceedings of the 2024 International conference on electrical, electronics and computing technologies (ICEECT 2024), 29-31 August 2024, Greater Noida, India. Piscataway: IEEE [online], article 10739123. Available from: https://doi.org/10.1109/ICEECT61758.2024.10739123

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2024 International conference on electrical, electronics and computing technologies (ICEECT 2024)
• Start Date: Aug 29, 2024
• End Date: Aug 31, 2024
• Acceptance Date: Jul 11, 2024
• Online Publication Date: Aug 29, 2024
• Publication Date: Dec 31, 2024
• Deposit Date: Jul 16, 2024
• Publicly Available Date: Aug 29, 2024
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1109/ICEECT61758.2024.10739123
• Keywords: Health informatics; Health technologies; Health analytics; Machine learning; Artificial intelligence (AI); Explainable artificial intelligence (XAI)
• Public URL: https://rgu-repository.worktribe.com/output/2413179