Fuzzy expert system for prediction of prostate cancer
This provides a supportive decision-making tool for medical diagnosis in prostate cancer, but it is incremental as it applies an existing fuzzy expert system method to a specific clinical dataset.
The authors tackled prostate cancer prediction by developing a fuzzy expert system that uses age, PSA, prostate volume, and % Free PSA as inputs to output cancer risk, achieving 68.91% overall prediction accuracy and 73.77% for positive biopsy cases.
A fuzzy expert system (FES) for the prediction of prostate cancer (PC) is prescribed in this article. Age, prostate-specific antigen (PSA), prostate volume (PV) and $\%$ Free PSA ($\%$FPSA) are fed as inputs into the FES and prostate cancer risk (PCR) is obtained as the output. Using knowledge based rules in Mamdani type inference method the output is calculated. If PCR $\ge 50\%$, then the patient shall be advised to go for a biopsy test for confirmation. The efficacy of the designed FES is tested against a clinical data set. The true prediction for all the patients turns out to be $68.91\%$ whereas only for positive biopsy cases it rises to $73.77\%$. This simple yet effective FES can be used as supportive tool for decision making in medical diagnosis.