Development of a prediction model for pancreatic cancer in patients with type 2 diabetes using logistic regression and artificial neural network models
Open accessAbstract
Objectives: Patients with type 2 diabetes (T2DM) are suggested to have a higher risk of developing pancreatic cancer. We used two models to predict pancreatic cancer risk among patients with T2DM. Methods: The original data used for this investigation were retrieved from the National Health Insurance Research Database of Taiwan. The prediction models included the available possible risk factors for pancreatic cancer. The data were split into training and test sets: 97.5% of the data were used as the training set and 2.5% of the data were used as the test set. Logistic regression (LR) and artificial neural network (ANN) models were implemented using Python (Version 3.7.0). The F 1 , precision, and recall were compared between the LR and the ANN models. The areas under the receiver operating characteristic (ROC) curves of the prediction models were also compared. Results: The metrics used in this study indicated that the LR model more accurately predicted pancreatic cancer than the ANN model. For the LR model, the area under the ROC curve in the prediction of pancreatic cancer was 0.727, indicating a good fit. Conclusion: Using this LR model, our results suggested that we could appropriately predict pancreatic cancer risk in patients with T2DM in Taiwan. Keywords: pancreatic cancer, type 2 diabetes, logistic regression, artificial neural network
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Further insurance-claim models have been developed and have poor diagnostic performance (AUC 0.73).
“Further models based on insurance claims have been developed, with poor diagnostic performance (AUC 0·73).106,107”
From Early detection of pancreatic cancer (2020) · cites this paper as [107]
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- Authors
- Hsieh, M. H., Sun, L.-M., Lin, C.-L., Hsieh, M.-J., Hsu, C.-Y., & Kao, C.-H.
- Affiliations
- Poznan University of Medical Sciences (PL)
- Zuoying Armed Forces General Hospital (TW)
- China Medical University (TW)
- Asia University (TW)
- Corresponding author
- Kao, Chia-Hung
- Venue
- Cancer Management and Research
- Publisher
- Informa UK Limited
- Published
- 2018-11
- Volume
- Volume 10
- Pages
- 6317-6324
- ISSN
- 1179-1322 (ISSN-L)
- PMC
- PMC6267763
- OpenAlex
- W2902936037
- Cited by
- 71
- References
- 35
- Citations by year
- 2026: 4 · 2025: 14 · 2024: 7 · 2023: 15 · 2022: 10 · 2021: 10 · 2020: 9 · 2019: 2
- FWCI
- 6.62
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Cancer Management and Research (publishedVersion, cc-by-nc)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Dove Medical Press (Taylor and Francis Group) (submittedVersion, cc-by-nc)
- Funders
- Ministry of Health and Welfare (MOHW107-TDU-B-212-123004, BM10701010021, DMR-107-192)
- Academia Sinica (DMR-107-192, BM10701010021, MOHW107-TDU-B-212-123004)
- China Medical University Hospital ((BM10701010021), MOHW107‐TDU‐B‐212‐123004, DMR-107–192)
- China Medical University (BM10701010021, MOHW107-TDU-B-212-123004)
- Division of Materials Research
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Oncology › Pancreatic and Hepatic Oncology Research
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Keywords
- pancreatic cancer, type 2 diabetes, cancer risk prediction, risk factors, logistic regression, artificial neural networks, forecasting model comparison, Taiwan
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