Enhancing Prostate Cancer Risk Prediction Using a Hybrid Near Sets and Soft Sets Model: A Novel Approach for Improved Patient Care

Abstract

Prostate cancer is a major health concern, and accurate risk prediction is essential for effective treatment. This paper presents a novel hybrid model combining near sets and soft sets to enhance prostate cancer risk assessment. By integrating artificial intelligence with medical data, our model captures uncertainties and provides more precise, personalized risk evaluations. Experiments focusing on key clinical factors, such as age and PSA levels, demonstrate significant improvements in early detection and treatment decisions. This research highlights the potential of hybrid AI models to improve patient care and outcomes in oncology.