Unravelling Colorectal Cancer Survival Factors through Explainable AI in Clinical Data

One of the significant issues affecting public health is Colorectal cancer (CRC) that underlines the necessity of establishing accurate survival predictive models for improving patient care and clinical decision-making. The current study develops a framework based on machine learning, which makes predictions using clinical and survival data, leveraging Explainable AI (XAI) methods to improve interpretability. The dataset, which consisted of various phenotypic and survival data, formed the basis of predictive model development and testing. The predictive models developed consisted of Random Forest and XGBoost, which made an ensemble using a weighted averaging mechanism (60% contributed by Random Forest and 40% by XGBoost). The resulting ensemble accurately predicted by 87.6% with 0.823 as the area under the curve, reflecting its capability to identify complex patterns in clinical datasets. To identify prognostic factors and improve transparency, Shapley Additive Explanations (SHAP) were used that substantially impact survival predictions. However, data heterogeneity, richness, gaps, and computation costs remain significant barriers. Future studies must focus on improving models' scalability, integrating deep learning algorithms, and using clinical data in real-world scenarios for precision medicine. Additionally, regulatory approval and improving interdisciplinary communication among bioinformaticians, oncologists, and data scientists are necessary for making AI-powered survival predictive models widely accessible. The integration of XAI in CRC studies can potentially provide actionable clinical insights, enhance patient stratification, and facilitate individualized treatment planning, strengthening AI's position in precision medicine in the oncology arena.