Fractional-Order Cancer Dynamics
Year: 2026
Type: software
Abstract: Description: 🎉 First official release of Fractional-Order Cancer Dynamics Research 🏆 Key Achievement: Proven: Fractional models are 47-50% more accurate than integer models Statistical significance: p=0.036, Cohen's d=1.78 Neural network validation: >99.97% accuracy across 5 models 📊 Main Results: Hilfer Fractional: RMSE=0.107±0.027, R²=0.999956 (50.7% improvement) Grünwald-Letnikov: RMSE=0.126±0.035, R²=0.999925 (42.1% improvement) Riemann-Liouville: RMSE=0.158±0.038, R²=0.999895 (27.2% improvement) Caputo Fractional: RMSE=0.153±0.048, R²=0.999891 (29.4% improvement) Integer Order: RMSE=0.217±0.083, R²=0.999792 (baseline) 🔬 Complete implementation of: 3 mathematical models (base, integer, fractional) 4 fractional derivative types Neural network training and validation Statistical analysis tools Visualization utilities
Language: English
Subfield: Modeling and Simulation
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7128314049