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A Neutrosophic Cubic Hesitant Fuzzy Decision Support System, Application in the Diagnosis and Grading of Prostate Cancer

According to available estimates with WHO, cancers are the sixth leading cause of global human morbidity and mortality. Prostate Cancer is the fifth-ranked most lethal among various cancers, and hence it warrants serious, dedicated research for improving its early detection. The employed methodologies such as prostate-specific antigen test, Gleason Score, and T2 Staging lack precision and accuracy in conditions where information is scarring, vague and uncertain. Consequently, in the present study, the innovative use of neutrosophic cubic fuzzy sets (NCFS) is employed to improve prostate cancer detection in situations where basic information is vague, imprecise, and uncertain. Specific and critical similarity measures are defined for using NCFS methodology for the evaluation of prostate cancer. This methodology is found reasonably better compared to the existing benchmark methods for the detection and grading of prostate cancer.