Clinical significance of risk factor analysis in pancreatic cancer by using supervised model of machine learning
HbA1c, ALP, CA19-9, CEA, and BMI were independently associated with pancreatic cancer. The machine learning-derived risk scoring model demonstrated high predictive accuracy and may serve as a valuable clinical tool for early detection and screening of pancreatic cancer.
Authors
- Sujit Kumar Mandal (ORCID: https://orcid.org/0000-0001-9840-5257)
- Binit Upadhaya Regmi (ORCID: https://orcid.org/0000-0002-9601-2457)
- Sandesh Raj Upadhaya (ORCID: https://orcid.org/0000-0001-6884-8161)
- Bishnu Prasad Gautam (ORCID: https://orcid.org/0000-0001-7539-3994)
- Amir Sherchan
- Maoquan Li (ORCID: https://orcid.org/0000-0002-2087-6769)
- Feng Jin (ORCID: https://orcid.org/0000-0002-4450-6084)
- Bhakti Sherchan
- Darshana Pathak
- Ranita Ghising
Institutions
- Tongji University (CN)
- Patan Academy of Health Sciences (NP)
- Patan Hospital (NP)
- Scheer Memorial Adventist Hospital (NP)
- Grande International Hospital (NP)
- Shanghai Pulmonary Hospital (CN)
- Shanghai Tenth People's Hospital (CN)
- Civil Service Hospital (NP)
Publication Details
- Journal
- Frontiers in Medicine
- Published
- 2025-05-26
- DOI
- https://doi.org/10.3389/fmed.2025.1551926
- Citations
- 2
- Primary Topic
- Pancreatic and Hepatic Oncology Research
- Type
- article
- Field-Weighted Citation Impact
- 2.09
Funders
- National Natural Science Foundation of China