A Hybrid Model for Prostate Cancer Detection and Prognosis: A Systematic Review

This study systematically reviews the integration of Interval Type-2 Fuzzy Logic (IT2FL) and XGBoost for prostate cancer detection and prognosis. Prostate cancer is a significant health concern, with existing diagnostic methods often challenged by data uncertainties and accuracy limitations. IT2FL effectively addresses uncertainties in medical data, while XGBoost enhances classification performance. The methodology involved a systematic search across databases, including Google Scholar, IEEE Xplore, Elsevier, and Springer, identifying 121 studies from an initial pool of 500 articles. The review investigates four key aspects: the integration of IT2FL and XGBoost, input parameter significance, the role of linguistic variables, and the impact of XGBoost on diagnostic accuracy. Visualization tools such as VosViewer and Matplotlib facilitated data analysis and presentation. Key findings highlight the hybrid model's ability to enhance diagnostic precision, reduce false positives, and support informed clinical decision-making. However, challenges like data diversity and model interpretability persist. The study highlights the potential of the hybrid model in advancing precision medicine by enhancing the detection and prognosis of prostate cancer. This paper demonstrates the feasibility and effectiveness of combining IT2FL and XGBoost, paving the way for future integration of explainable AI techniques and broader clinical applications.