Advanced Computational Modeling and Machine Learning for Risk Stratification, Treatment Optimization, and Prognostic Forecasting in Appendiceal Neoplasms
By integrating multi-database evidence with advanced modeling and statistical methodologies, our findings demonstrate valuable insights and implications for individualized prognosis, better management decision-making, and health system planning. Our proposed approach and demonstrated methodologies are warranting better progression and advancements in precision oncology and utilization of computational modeling techniques in big data as well as digital health progression landscape.
Authors
- Abdulmalik Alsaif
- Maha A. Alghamdi (ORCID: https://orcid.org/0000-0003-1306-9616)
- Mohammed N AlAli (ORCID: https://orcid.org/0000-0001-9011-7357)
- Manal Alquaimi (ORCID: https://orcid.org/0000-0002-8788-5619)
- Arwa F Alrasheed (ORCID: https://orcid.org/0009-0003-8915-2296)
- Ahmed Y. Azzam (ORCID: https://orcid.org/0000-0002-4256-0159)
- Jawad S Alnajjar (ORCID: https://orcid.org/0009-0006-0327-1764)
- Faisal A. Al-Harbi (ORCID: https://orcid.org/0009-0008-7741-4670)
- Omar K. Aljubaili
- Ghaida S. Alabdulaaly (ORCID: https://orcid.org/0009-0009-9609-0779)
Institutions
- Qassim University (SA)
- Beebe Healthcare (US)
- Al-Ghad International Health Sciences Colleges (SA)
- Prince Mohammed bin Abdulaziz Hospital (SA)
- King Faisal University (SA)
- King Khalid University (SA)
Publication Details
- Journal
- Healthcare
- Published
- 2025-11-26
- DOI
- https://doi.org/10.3390/healthcare13233074
- Primary Topic
- Intraperitoneal and Appendiceal Malignancies
- Type
- article
- Field-Weighted Citation Impact
- 0.00