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. 2025 May 26:12:1551926.
doi: 10.3389/fmed.2025.1551926. eCollection 2025.

Clinical significance of risk factor analysis in pancreatic cancer by using supervised model of machine learning

Amir Sherchan  1 , Feng Jin  1 , Bhakti Sherchan  2 , Sujit Kumar Mandal  3 , Binit Upadhaya Regmi  4 , Ranita Ghising  2 , Sandesh Raj Upadhaya  5 , Bishnu Gautam  6 , Dipendra Pathak  7 , Maoquan Li  1
Affiliations

Clinical significance of risk factor analysis in pancreatic cancer by using supervised model of machine learning

Amir Sherchan et al. Front Med (Lausanne). .

Abstract

Introduction: 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.

Methods: 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.

Results: Key predictors retained in the final logistic regression model included Hemoglobin A1c (HbA1c) (OR 1.28; 95% CI: 1.08-1.52), Alkaline Phosphatase (ALP) (OR 1.02; 95% CI: 1.01-1.03), CA19-9 (OR 1.01; 95% CI: 1.01-1.01), Carcinoembryonic Antigen (CEA) (OR 1.41; 95% CI: 1.20-1.66), and Body Mass Index (BMI) (OR 0.88; 95% CI: 0.81-0.97). The final model demonstrated excellent diagnostic performance (AUC = 0.969, p < 0.001), with high accuracy, sensitivity, and specificity. A nomogram was constructed to facilitate individualized PC risk assessment.

Conclusion: 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.

Keywords: machine learning; pancreatic cancer; risk factors; risk scoring; supervised model.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Study flow chart.
FIGURE 2
FIGURE 2
Cluster dendrogram for categorical variables.
FIGURE 3
FIGURE 3
Cluster dendrogram for continuous variables.
FIGURE 4
FIGURE 4
Calibration plot of the internal validation model from logistic regression.
FIGURE 5
FIGURE 5
ROC curve for the final model from logistic regression.
FIGURE 6
FIGURE 6
Nomogram of the model from logistic regression.
See this image and copyright information in PMC

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