<![CDATA[Prostate cancer is one of the malignancies most commonly affecting men, particularly older adults, and remains a major contributor to cancer morbidity and mortality worldwide. Diagnosing prostate cancer requires interpreting multiple clinical parameters that are often uncertain and gradual in nature, which complicates medical decision-making. This study aims to design and implement a web-based expert system for prostate cancer risk diagnosis using the Tsukamoto fuzzy logic method. The system was developed with the Laravel 10 framework, PHP 8.1, and a MySQL 8.0 database, using four clinical input variables: Prostate-Specific Antigen (PSA) level, patient age, International Prostate Symptom Score (IPSS), and PSA density. A knowledge base of 25 fuzzy rules was formulated through iterative interviews with urology specialists and validated against 50 electronic medical record cases from a referral hospital covering the 2021–2024 period. The inference process follows the Tsukamoto method through fuzzification, rule evaluation using the minimum operator, and weighted-average defuzzification, producing a risk percentage classified into low, medium, or high categories. Black-box testing of eight functional scenarios achieved a 100% success rate. Accuracy testing on 30 of these 50 cases, validated by a urology specialist, yielded an overall accuracy of 90%, with 27 of 30 cases correctly classified. These results indicate that the developed expert system can effectively support early screening of prostate cancer risk and serve as a decision-support tool for healthcare providers, particularly in facilities with limited access to urology specialists.]]>
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