Incorporation of explainable artificial intelligence in ensemble machine learning-driven pancreatic cancer diagnosis
Walczak, S. & Velanovich, V. Improving prognosis and reducing decision regret for pancreatic cancer treatment using artificial neural networks. Decis. Support Syst. 106, 110–118 (2018).
Google Scholar
Cancer Today. https://gco.iarc.fr/today/online-analysis-table?v=2020&mode=cancer&mode_population=continents&population=900&populations=900&key=asr&sex=0&cancer=39&type=1&statistic=5&prevalence=0&population_group=0&ages_group%5B%5D=0&ages_group%5B%5D=17&group_cancer=1&include_nmsc=0&include_nmsc_other=1.
Ghaneh, P., Costello, E. & Neoptolemos, J. P. Biology and management of pancreatic cancer. Postgrad. Med. J. 84, 478–497 (2008).
Google Scholar
Brand, R. E. et al. Imaging, diagnosis, prognosis serum biomarker panels for the detection of pancreatic cancer. Clin. Cancer Res. 17.
Chan, A., Diamandis, E. P. & Blasutig, I. M. Strategies for discovering novel pancreatic cancer biomarkers. J. Proteom. 81, 126–134 (2013).
Google Scholar
Zhou, Y. et al. Evaluation of urinary metal concentrations and sperm DNA damage in infertile men from an infertility clinic. Environ. Toxicol. Pharmacol. 45, 68–73 (2016).
Google Scholar
Khomiak, A. et al. Recent discoveries of diagnostic, prognostic and predictive biomarkers for pancreatic cancer. Cancers 2020. 12, 3234 (2020).
Google Scholar
Pu, X., Sheng, S., Fu, Y., Yang, Y. & Xu, G. Construction of circRNA–miRNA–mRNA CeRNA regulatory network and screening of diagnostic targets for tuberculosis. Ann. Med. 56, 2416604 (2024).
Google Scholar
Li, R., Luo, P., Guo, Y., He, Y. & Wang, C. Clinical features, treatment, and prognosis of SGLT2 inhibitors induced acute pancreatitis. Expert Opin. Drug Saf. (2024).
Google Scholar
Fang, W., Sun, W., Fang, W., Zhang, J. & Wang, C. Clinical features, treatment, and outcome of pembrolizumab-induced cholangitis. Naunyn Schmiedebergs Arch. Pharmacol. 397, 7905–7912 (2024).
Google Scholar
Luo, P., Guo, Y., He, Y. & Wang, C. Clinical characteristics, treatment and outcome of pembrolizumab-induced acute pancreatitis. Invest. New. Drugs. 42, 369–375 (2024).
Google Scholar
Balcioglu, O., Usanase, N., Uzun, B., Ozsahin, I. & Uzun Ozsahin, D. A comparative analysis of DOACs vs warfarin for venous thromboembolism treatment in renal insufficiency. Turkish J. Vascular Surg.. (2023).
Google Scholar
Ozsahin, D. U., Usanase, N., Uzun, B., Ozsahin, I. & Balcioglu, O. The Efficacy and Safety of Direct Oral Anticoagulants for The Treatment of Venous Thrombosis in Cancer Using Fuzzy PROMETHEE. 2023 Advances in Science and Engineering Technology International Conferences (ASET) 1–5 (2023). https://doi.org/10.1109/ASET56582.2023.10180505
Usanase, N., Uzun, B., Ozsahin, D. U. & Ozsahin, I. A look at radiation detectors and their applications in medical imaging. Jpn J. Radiol. 1, 1–13 (2023).
Ozsahin, I., Usanase, N., Uzun, B., Ozsahin, D. U. & Mustapha, M. T. A mathematical resolution in selecting suitable magnetic field-based breast cancer imaging modality: A comparative study on seven diagnostic techniques. Artif. Intell. Image Process. Med. Imaging. (2024).
Google Scholar
Ozsahin, I. et al. BI-RADS-based classification of breast cancer mammogram dataset using six stand-alone machine learning algorithms. Artif. Intell. Image Process. Med. Imaging. (2024).
Google Scholar
Uzun Ozsahin, D., Ikechukwu Emegano, D., Uzun, B. & Ozsahin, I. The systematic review of artificial intelligence applications in breast cancer diagnosis. Diagnostics 2023. 13, 45 (2022).
Mahawan, T., Luckett, T., Mielgo Iza, A. & Pornputtapong, N. Caamaño Gutiérrez, E. Robust and consistent biomarker candidates identification by a machine learning approach applied to pancreatic ductal adenocarcinoma metastasis. BMC Med. Inf. Decis. Mak. 24, 1–16 (2024).
Pu, X. et al. Diagnostic plasma small extracellular vesicles MiRNA signatures for pancreatic cancer using machine learning methods. Transl Oncol. 40, (2024).
Construction of Classifier Based on MPCA and QSA and Its Application on Classification of Pancreatic Diseases. https://www.hindawi.com/journals/cmmm/2013/713174/
Dang, C., Bian, Q., Wang, F., Wang, H. & Liang, Z. Machine learning identifies SLC6A14 as a novel biomarker promoting the proliferation and metastasis of pancreatic cancer via Wnt/β-catenin signaling. Scientific Reports 2024 14:1 14, 1–21 (2024).
Huang, B., Xin, C., Yan, H. & Yu, Z. A machine learning method for a blood diagnostic model of pancreatic cancer based on MicroRNA signatures. Crit. Rev. Immunol. 44, 13–23 (2024).
Google Scholar
Kaya, M. & Bilge, H. Ş. Classification of pancreas tumor dataset using adaptive weighted K nearest neighbor algorithm. INISTA 2014 – IEEE Int. Symp. Innovations Intell. Syst. Appl. Proc. 253–257 (2014).
Hayward, J. et al. Machine learning of clinical performance in a pancreatic cancer database. Artif. Intell. Med. 49, 187–195 (2010).
Google Scholar
Alaca, Y. Machine learning via DARTS-Optimized mobilevit models for pancreatic cancer diagnosis with graph-based deep learning. BMC Med. Inf. Decis. Mak. 25, 81 (2025).
Google Scholar
Bakasa, W., Kwenda, C. & Viriri, S. Hybrid deep learning model for pancreatic cancer image segmentation. 14–24 (2025). https://doi.org/10.1007/978-3-031-73483-0_2
Li, S., Jiang, H., Wang, Z., Zhang, G. & Yao Dong, Y. An effective computer-aided diagnosis model for pancreas cancer on PET/CT images. Comput. Methods Programs Biomed. 165, 205–214 (2018).
Google Scholar
Mikdadi, D. et al. Applications of artificial intelligence (AI) in ovarian cancer, pancreatic cancer, and image biomarker discovery. Cancer Biomarkers vol. 33 173–184 Preprint at (2022). https://doi.org/10.3233/CBM-210301
Acer, İ., Orhanbulucu, F., Içer, S. & Latifoglu, F. Early diagnosis of pancreatic cancer by machine learning methods using urine biomarker combinations. Turkish J. Electr. Eng. Comput. Sci. 31, 112–125 (2023).
Google Scholar
Severeyn, E., La Cruz, A., Velásquez, J. & Huerta, M. Early Diagnosis of Pancreatic Cancer using Urinary Biomarkers and Machine Learning. ETCM 2024–8th Ecuador Technical Chapters Meeting (2024). https://doi.org/10.1109/ETCM63562.2024.10746008
Ghosh, S., Swetapadma, A., Nayak, S. S. & Sahoo, B. Machine Learning-Based analysis for detection of pancreatic adenocarcinoma using urinary biomarkers. Adv. Healthc. Through Data-driven Innovations. (2024).
Google Scholar
Kumar, K. P. & Gayathri, A. An effective way to detect urinary biomarkers for pancreatic cancer using a random forest algorithm with enhanced accuracy compared to the XGBoost algorithm. AIP Conf. Proc. 3252, 020029 (2025).
Google Scholar
Debernardi, S. et al. A combination of urinary biomarker panel and pancrisk score for earlier detection of pancreatic cancer: A case-control study. PLoS Med. 17, e1003489 (2020).
Google Scholar
Zhang, S., Hu, Z., Ye, L. & Zheng, Y. Application of logistic regression and decision tree analysis in prediction of acute myocardial infarction events. Zhejiang Da Xue Xue Bao Yi Xue Ban. 48, 594–602 (2019).
Google Scholar
Xu, Y., Klein, B., Li, G. & Gopaluni, B. Evaluation of logistic regression and support vector machine approaches for XRF-based particle sorting for a copper ore. Min. Eng. 192, 108003 (2023).
Google Scholar
Schonlau, M. & Zou, R. Y. The random forest algorithm for statistical learning. Stata J. 20, 3–29 (2020).
Google Scholar
Supervised Machine Learning Algorithms. Classification and Comparison. https://www.ijcttjournal.org/archives/ijctt-v48p126
Sailasya, G. & Kumari, G. L. A. Analyzing the performance of stroke prediction using ML classification algorithms. Int. J. Adv. Comput. Sci. Appl. 12, 539–545 (2021).
Verma, G. & Sahu, T. P. A correlation-based feature weighting filter for multi-label Naive Bayes. Int. J. Inform. Technol. (Singapore). 16, 611–619 (2024).
Fadlil, A., Riadi, I. & Purwadi Putra, I. J. D. E. Comparison of machine learning performance using Naive Bayes and random forest methods to classify Batik fabric patterns. Revue d’Intelligence Artificielle. 37, 379–385 (2023).
Google Scholar
Kavya, R., Christopher, J., Panda, S. & Lazarus, Y. B. Machine learning and XAI approaches for allergy diagnosis. Biomed. Signal. Process. Control. 69, 102681 (2021).
Google Scholar
Shapley, L. 7. A Value for n-Person Games. Contributions to the Theory of Games II 307–317. Classics in Game Theory 69–79 (2021) (1953). https://doi.org/10.1515/9781400829156-012/PDF
Yagin, F. H. et al. Explainable artificial intelligence model for identifying COVID-19 gene biomarkers. Comput. Biol. Med. 154, 106619 (2023).
Google Scholar
Messalas, A., Kanellopoulos, Y. & Makris, C. Model-Agnostic interpretability with Shapley values. 10th Int. Conf. Inform. Intell. Syst. Appl. IISA 2019. (2019).
Google Scholar
Ramachandra, H. V., Chavan, P., Ali, A. & Ramaprasad, H. C. Ensemble Machine Learning Techniques for Pancreatic Cancer Detection. International Conference on Applied Intelligence and Sustainable Computing, ICAISC 2023 (2023). https://doi.org/10.1109/ICAISC58445.2023.10200380
Nené, N. R. et al. Serum biomarker-based early detection of pancreatic ductal adenocarcinomas with ensemble learning. Communications Medicine 2023 3:1 3, 1–14 (2023).
Reddy, P. S. & Chandrasekar, M. P. A. D. A pancreatic cancer detection based on extracted medical data through ensemble methods in machine learning. Int. J. Adv. Comput. Sci. Appl. 13, 149–156 (2022).
Bakasa, W. & Viriri, S. VGG16 feature extractor with extreme gradient boost classifier for pancreas cancer prediction. J. Imaging 2023. 9, 138 (2023).
Yadav, K. et al. Early Detection of Pancreatic Cancer Using Ensemble Learning with Medical Imaging. 10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering, UPCON 2023 1846–1852 (2023) (2023). https://doi.org/10.1109/UPCON59197.2023.10434602
Lee, H. A., Chen, K. W. & Hsu, C. Y. Prediction model for pancreatic Cancer—A Population-Based study from NHIRD. Cancers 2022. 14, 882 (2022).
Li, J. et al. XGBoost classifier based on computed tomography radiomics for prediction of Tumor-Infiltrating CD8 + T-Cells in patients with pancreatic ductal adenocarcinoma. Front. Oncol. 11, 671333 (2021).
Google Scholar
ALPU, Ö. & PEKDEMİR, G. The classification capability of urine biomarkers in the diagnosis of pancreatic cancer with logistic regression based on regularized approaches: A methodological research. Turkiye Klinikleri J. Biostatistics. 14, 118–128 (2022).
Google Scholar
Laxminarayanamma, K., Krishnaiah, R. V. & Sammulal, P. Enhanced CNN model for pancreatic ductal adenocarcinoma classification based on proteomic data. Ingenierie Des. Systemes d’Information. 27, 127–133 (2022).
Blyuss, O. et al. Development of pancrisk, a urine biomarker-based risk score for stratified screening of pancreatic cancer patients. Br. J. Cancer. 122, 692–696 (2020).
Google Scholar
Gerdtsson, A. S. et al. Plasma protein profiling in a stage defined pancreatic cancer cohort – Implications for early diagnosis. Mol. Oncol. 10, 1305–1316 (2016).
Google Scholar
Iwatate, Y. et al. Radiogenomics for predicting p53 status, PD-L1 expression, and prognosis with machine learning in pancreatic cancer. British Journal of Cancer 2020 123:8 123, 1253–1261 (2020).
Gress, T. M. et al. Combined MicroRNA and mRNA microfluidic TaqMan array cards for the diagnosis of malignancy of multiple types of pancreaticobiliary tumors in fine-needle aspiration material. Oncotarget 8, 108223–108237 (2017).
Google Scholar
Chen, W. et al. Derivation and external validation of machine Learning-Based model for detection of pancreatic cancer. Am. J. Gastroenterol. 118, 157–167 (2023).
Google Scholar
Chen, W. et al. Risk prediction of pancreatic cancer in patients with Recent-onset hyperglycemia: A Machine-learning approach. J. Clin. Gastroenterol. 57, 103–110 (2023).
Google Scholar
Malhotra, A., Rachet, B., Bonaventure, A., Pereira, S. P. & Woods, L. M. Can we screen for pancreatic cancer? Identifying a sub-population of patients at high risk of subsequent diagnosis using machine learning techniques applied to primary care data. PLoS One. 16, e0251876 (2021).
Google Scholar
Shelly, M. & Sivakumar, S. Enhancing pancreatic cancer diagnostics: Ensemble-based model for automated urine biomarker classification. Comput. Biol. Med. 189, 109997 (2025).
Google Scholar
Hegde, S. K., Hegde, R., Murugan, T. & XGSVM Based Ensemble Machine Learning Model For The Early Prediction of Pancreatic Cancer. 2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024 (2024). https://doi.org/10.1109/NMITCON62075.2024.10699092
Ganie, S. M. & Malik, M. B. An ensemble machine learning approach for predicting Type-II diabetes mellitus based on lifestyle indicators. Healthc. Analytics. 2, 100092 (2022).
Google Scholar
Naderalvojoud, B. & Hernandez-Boussard, T. Improving machine learning with ensemble learning on observational healthcare data. AMIA Annual Symposium Proceedings 521 (2024). (2023).
Baba, S. et al. A noninvasive urinary microRNA-based assay for the detection of pancreatic cancer from early to late stages: a case-control study. EClinicalMedicine 78, 102936 (2024).
Google Scholar
Patel, H. Y. & Mukherjee, I. A. Novel neural network to predict locally advanced pancreatic cancer using 4 urinary biomarkers: REG1A/1B, LYVE1, and TFF1. J. Am. Coll. Surg. 235, S144–S145 (2022).
Google Scholar
Karar, M. E., El-Fishawy, N. & Radad, M. Automated classification of urine biomarkers to diagnose pancreatic cancer using 1-D convolutional neural networks. J. Biol. Eng. 17, 1–12 (2023).
Google Scholar
Sun, J., Sun, C. K., Tang, Y. X., Liu, T. C. & Lu, C. J. Application of SHAP for explainable machine learning on Age-Based subgrouping mammography questionnaire data for positive mammography prediction and risk factor identification. Healthc. 2023. 11, 2000 (2023).
Książek, W. Explainable thyroid cancer diagnosis through Two-Level machine learning optimization with an improved naked Mole-Rat algorithm. Cancers (Basel). 16, 4128 (2024).
Google Scholar
Alabi, R. O., Elmusrati, M., Leivo, I., Almangush, A. & Mäkitie, A. A. Machine learning explainability in nasopharyngeal cancer survival using LIME and SHAP. Scientific Reports 2023 13:1 13, 1–14 (2023).
Zhen, J. et al. Development and validation of machine learning models for young-onset colorectal cancer risk stratification. Npj Precision Oncol. 2024. 8:1 8, 1–14 (2024).
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