ORIGINAL RESEARCH article
Front. Med.
Sec. Gastroenterology
Volume 12 - 2025 | doi: 10.3389/fmed.2025.1551926
CLINICAL SIGNIFICANCE OF RISK FACTOR ANALYSIS IN PANCREATIC CANCER BY USING SUPERVISED MODEL OF
Provisionally accepted
Amir Sherchan1
Jin Feng1Bhakti Sherchan2
Sujit Kumar Mandal3
Binit Upadhaya Regmi4Ranita Ghising2Sandesh Raj Upadhaya5Bishnu gautam6Dipendra Pathak7
Maoquan Li1*- 1Department of Radiology, Shanghai Tenth People′s Hospital, Tongji University, Shanghai, Jiangsu Province, China
- 2Department of General Surgery, Scheer Memorial Adventist Hospital, Kavre, Nepal
- 3District Hospital, Doti, Doti, Nepal
- 4Patan Hospital, Patan Academy of Health Sciences, Kathmandu, Nepal
- 5Trishuli Hospital, Nuwakot, Nepal
- 6Department of Radiology, Buddha International Hospital, Dang, Nepal
- 7Department of Radiology, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China
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Pancreatic cancer (PC) poses a significant global health challenge due to its aggressive nature, late-stage diagnosis, and high mortality despite advancements in treatment. Early detection remains crucial for timely intervention. This study aimed to identify clinically relevant predictors of pancreatic cancer using a supervised machine learning approach and to develop a risk stratification tool with diagnostic capabilities.A matched case-control study was conducted retrospectively at the Tenth People's Hospital of Tongji University (2017-2023), involving 353 cases and 370 matched controls. Demographic and hematological data were extracted from medical records. Variables were pre-selected using cluster dendrograms and subsequently refined using logistic regression with backward elimination and Support Vector Machine (SVM) models. A final risk scoring model was developed based on the best-performing model and internally validated.Key predictors retained in the final logistic regression model included Hemoglobin A1c (HbA1c) (
Keywords: Pancreatic Cancer, Risk factors, Risk scoring, machine learning, supervised model
Received: 26 Dec 2024; Accepted: 22 Apr 2025.
Copyright: © 2025 Sherchan, Feng, Sherchan, Mandal, Regmi, Ghising, Upadhaya, gautam, Pathak and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Maoquan Li, Department of Radiology, Shanghai Tenth People′s Hospital, Tongji University, Shanghai, 200070, Jiangsu Province, China
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