AI-Based Patient No-Show Prediction using Machine Learning

This research explores the use of Machine Learning techniques to predict patient no-shows in healthcare systems. Using a Logistic Regression model on a real-world dataset, the study achieved approximately 80% accuracy. The findings highlight the potential of AI in improving hospital efficiency and reducing missed appointments.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-03-31
DOI
https://doi.org/10.5281/zenodo.19351133
Primary Topic
Healthcare Operations and Scheduling Optimization
Type
article
Field-Weighted Citation Impact
0.00
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article

AI-Based Patient No-Show Prediction using Machine Learning

Md. Khalid Hasan
Zenodo (CERN European Organization for Nuclear Research)
Healthcare Operations and Scheduling Optimization
article

AI-Based Patient No-Show Prediction using Machine Learning

Md. Khalid Hasan
article en

Abstract

This research explores the use of Machine Learning techniques to predict patient no-shows in healthcare systems. Using a Logistic Regression model on a real-world dataset, the study achieved approximately 80% accuracy. The findings highlight the potential of AI in improving hospital efficiency and reducing missed appointments.

Zenodo (CERN European Organization for Nuclear Research)
Bangladesh University of Professionals (BD)
Openalex Percentile: Top 5%
Healthcare Operations and Scheduling Optimization
DOIOpen Access PDFOpenAlex
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AI-Based Patient No-Show Prediction using Machine Learning — Md. Khalid Hasan · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS