Academic Journal
A genetic algorithm-optimized EWMA control chart integrating machine learning-based risk prediction for multistage thyroid cancer treatment monitoring.
| Τίτλος: | A genetic algorithm-optimized EWMA control chart integrating machine learning-based risk prediction for multistage thyroid cancer treatment monitoring. |
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| Συγγραφείς: | Rasouli M; Caspian Faculty of Engineering, College of Engineering, University of Tehran, Tehran, Iran., Rahimi E; Caspian Faculty of Engineering, College of Engineering, University of Tehran, Tehran, Iran., Ilaty A; Donald Bren School of Information and Computer Sciences, University of California Irvine, Irvine, California, United States of America. |
| Πηγή: | PloS one [PLoS One] 2026 Oct 06; Vol. 21 (10), pp. e0347876. Date of Electronic Publication: 2026 Oct 06 (Print Publication: 2026). |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: San Francisco, CA : Public Library of Science |
| Ιατρικοί όροι (MeSH): | Thyroid Neoplasms*/therapy , Thyroid Neoplasms*/diagnosis , Thyroid Neoplasms*/genetics , Machine Learning*, Humans ; Genetic Algorithms ; Electronic Health Records ; Prediction Algorithms ; Multilayer Perceptrons ; Predictive Learning Models ; Algorithms ; Risk Assessment |
| Περίληψη: | Effective monitoring of thyroid cancer treatment requires timely detection of clinically meaningful changes across complex, multistage care pathways. Traditional monitoring approaches often rely on static thresholds and periodic assessments, limiting their sensitivity to subtle yet important process deviations and potentially delaying clinical intervention. This study aimed to develop and evaluate an integrated, data-driven framework that combines deep learning-based risk prediction with genetic algorithm-optimized statistical process monitoring to enhance thyroid cancer treatment surveillance. Informative clinical variables were first identified using the Information Functions algorithm, and a Multilayer Perceptron (MLP) neural network was then employed to predict patient-specific treatment risk, capturing nonlinear relationships within longitudinal electronic medical record data. The resulting risk estimates were sequentially incorporated into Exponentially Weighted Moving Average (EWMA) control charts to enable continuous process monitoring. To further improve chart performance, a genetic algorithm was used to optimize EWMA parameters, enhancing accuracy and sensitivity while maintaining chart stability. The results demonstrate that the MLP model outperforms conventional machine learning approaches in risk prediction across multiple evaluation metrics. Genetic algorithm-optimized EWMA charts exhibit smoother trajectories, improved stability, and more reliable detection of clinically meaningful deviations compared with non-optimized charts. The consistency of these improvements across multiple features suggests that GA-based optimization enhances overall control chart performance rather than feature-specific behavior. This hybrid framework demonstrates the feasibility of integrating machine learning-derived risk prediction with optimized statistical process control for longitudinal treatment monitoring. The proposed approach supports proactive treatment surveillance, risk-stratified follow-up, timely clinical intervention, and data-driven decision-making in thyroid cancer care. Furthermore, the framework has the potential to enable continuous and adaptive monitoring across complex multi-stage treatment pathways if integrated with electronic medical record (EMR) systems in future implementations. (Copyright: © 2026 Rasouli et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.) |
| Competing Interests: | The authors have declared that no competing interests exist. |
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| Entry Date(s): | Date Created: 20261006 Date Completed: 20261006 Latest Revision: 20261008 |
| Update Code: | 20261008 |
| PubMed Central ID: | PMC13641396 |
| DOI: | 10.1371/journal.pone.0347876 |
| PMID: | 42837336 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 1932-6203 |
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| DOI: | 10.1371/journal.pone.0347876 |