Development A Hybrid Model Based on Harris Hawks Optimization (HHO) Algorithm and Quantum Learning to Diagnosis of Prostate Cancer
Melisa Rahebi · Journal of Polytechnic · 2025
Prostate cancer (PC) represents a significant health problem and stands among the primary mortality causes in men, partly due to the drawbacks of the diagnostic techniques currently used. Annually, these current diagnosis techniques cause many men to lose their lives simply because they cannot get an accurate diagnosis on time. A potentially practical and cost-effective approach for diagnosing PC is applying artificial intelligence, particularly machine learning. This work is aimed at developing a machine learning (ML) model for the diagnosis of prostate cancer on clinical data of 100 men, among whom 38% were suffering and 62% were healthy. The proposed model combines the Harris Hawk Optimization (HHO) and Quantum Learning (QL) methods. The results reveal that this new approach provides better accuracy, 97.84%, than other ML approaches.