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.
Shengyu Gu· Journal of Computer Science...· 0 citations
Data-driven intelligent service platforms have evolved from simple service intermediaries into institutional governance infrastructures that shape decision-making, coordination, and accountability across sectors. This study examines how platform governance operates in tourism and healthcare systems, emphasizing the roles of data integration, algorithmic decision mechanisms, and institutional oversight. Rather than treating these platforms as domain-specific service tools, the analysis positions them as decision infrastructures that regulate participation, coordinate stakeholders, monitor performance, and enforce compliance through embedded governance mechanisms. Drawing on verifiable platform datasets and governance frameworks, the study demonstrates that tourism platforms historically pioneered large-scale data-driven governance through booking systems, mobility analytics, and review ecosystems. Healthcare platforms subsequently adopted similar governance architectures to support service coordination, regulatory compliance, and risk management in high-stakes institutional environments. Despite differences in sectoral context, both domains rely on the same core governance functions: regulation, coordination, monitoring, accountability, and optimization. The findings show that intelligent platforms enhance decision quality, institutional coordination, policy compliance, and risk governance by embedding regulatory logic into technical systems. Public trust is strengthened through transparent reporting, accountable platform operations, and responsible data stewardship. Healthcare is treated as a governance application case, while tourism represents the methodological origin of platform-based governance practices. Overall, this study contributes to platform governance and AI management research by demonstrating that data-driven platforms function as institutional governance systems rather than merely as service delivery technologies. It highlights the cross-domain transferability of platform governance logic and clarifies how big data and AI reshape contemporary institutional decision-making.
Shengyu Gu· Frontiers in Public Manageme...· 0 citations
This study proposes a unified governance framework for high-dimensional data-driven intelligent platforms by integrating insights from platform ecosystem theory, algorithmic governance, and data governance research, and reframes intelligent platforms as socio-technical governance systems.
Shengyu Gu· Frontiers in Public Manageme...· 0 citations