Deep Learning and XGBoost for Pancreatic Cancer Survival Prediction: A Real-World Evaluation in a Resource-Constrained African Healthcare Setting
DOI:
https://doi.org/10.30871/jaic.v10i3.12570Keywords:
Artificial Neural Networks, Pancreatic Cancer Survival Prediction, Resource-Constrained Healthcare, Supervised Machine Learning, XGBoostAbstract
Pancreatic cancer remains one of the most lethal malignancies worldwide, with persistently low survival rates and a pressing need for reliable prognostic tools to support treatment planning in resource-constrained healthcare environments. This study presents a structured comparative evaluation of Artificial Neural Network (ANN) and XGBoost classifiers for predicting 12-month survival using real-world clinical data from 569 pancreatic cancer patients treated at a public hospital in Zimbabwe between 2018 and 2023. The Cross-Industry Standard Process for Data Mining (CRISP-DM) framework guided data understanding, preprocessing, model development, and evaluation. A comprehensive preprocessing pipeline incorporating missing value imputation, outlier management, encoding, feature selection, and normalisation was applied, with all transformations derived exclusively from the training set to prevent data leakage. Models were trained using an 80/20 stratified split with cross-validated hyperparameter optimisation and evaluated on a strictly held-out test set using accuracy, precision, recall, F1-score, ROC analysis, and McNemar’s test. On the test dataset, the ANN model achieved 99% overall accuracy and 99% F1-score, outperforming XGBoost, which attained 90% accuracy and 90% F1-score. The performance difference was statistically significant (p < 0.05). Computational analysis demonstrated inference times below 3 milliseconds per sample, supporting feasibility for clinical deployment. While results indicate strong discriminative capacity within this single-centre dataset, external validation across multi-institutional cohorts is necessary to confirm generalisability. These findings suggest that supervised machine learning can provide clinically meaningful support for survival prediction in African tertiary healthcare settings. This study uniquely contributes a deployment-oriented, real-world evaluation of machine learning models within a resource-constrained African healthcare context, addressing a critical gap in the current oncology informatics literature.
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Copyright (c) 2026 Zvinodashe Revesai, Kudakwashe Maguraushe, Belinda Ndlovu
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