A multi-stage soft computing framework for complex disease modelling and decision support: A liver cirrhosis case study
Liver cirrhosis is a major global health problem causing millions of deaths annually, and timely detection with aggressive treatment can significantly improve patients' quality of life. Modelling complex diseases from biomedical data is computationally challenging due to high dimensionality, strong feature correlations, noise, and limited labelled samples. Conventional Machine Learning (ML) pipelines often struggle with robustness, interpretability, and generalisation under such conditions. In this study, we propose an ML-driven multi-stage decision framework for complex disease modelling and therapeutic exploration. The framework integrates single-cell transcriptomic profiling, high-dime...
| Authors | Xueyuan Huang, Yuheng Wang, Yuanzhi He et al. |
|---|---|
| Primary category | q-bio.OT |
| Published | 26 April 2026 |
| Last updated at source | 26 April 2026 |
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