Building Machine-Learning-Based Decision Support Models for Complex Systems: A Multi-Omics Healthcare Case Study
TL;DR
This study frames cancer classification as a complex decision-support problem rather than a purely medical task, and demonstrates that the same DSS logic applies across complex systems characterized by uncertainty, multi-source data, and nonlinear outcomes.
Abstract
This study frames cancer classification as a complex decision-support problem rather than a purely medical task. Drawing on theories of complex adaptive systems (CAS) and data-driven governance, we examine how machine-learning-based decision support systems (DSS) transform large-scale, multi-omics healthcare data into actionable risk stratification outputs. Public datasets from TCGA, CGGA, GEO, and the World Health Organization are used as a case context to evaluate algorithmic decision models, including Random Forest, Support Vector Machines, XGBoost, and SHAP-based explainability tools. Results show that these models achieve stable performance (accuracy 85-92%, AUC 0.88-0.95) while maintaining interpretability and decision reliability. By comparing healthcare analytics with tourism management and smart city governance, the study demonstrates that the same DSS logic applies across complex systems characterized by uncertainty, multi-source data, and nonlinear outcomes. The findings highlight the transferability of machine-learning-driven decision support methodologies to tourism destination management, urban governance, and other data-intensive policy domains.