Geneto-Neuro-Fuzzy Solution for Detection of Early Prostate Cancer

This study presents a novel hybrid diagnostic model for early-stage prostate cancer, integrating genetic algorithms, neuro-fuzzy logic, and mobile agent technology. By optimizing the selection of key genetic markers, the system achieves a marked improvement in sensitivity and specificity-up to a 12% increase over conventional diagnostic methods-based on extensive simulations and a controlled clinical dataset. The neuro-fuzzy component adaptively merges genetic, clinical, and historical patient data, refining its predictive accuracy through continuous learning. A decentralized computation strategy enhances scalability and lowers device-level computational overhead, supporting widespread implementation in diverse healthcare environments. While initial results underscore its promise, validation through large-scale clinical trials remains a priority. This approach offers a practical, high-accuracy solution that could significantly reduce late-stage prostate cancer diagnoses, particularly in resourcelimited settings, and stands as a step forward in precision oncology diagnostics. The developed approach can be helpful in diagnosis of the relevant healthcare problem.