For the purpose of system evaluation, 2500 data were collected from two health care centers in Nigeria using random sampling. ![]() Java expert system shell running on Android operating system was used to achieve the mobile technology aspect. The system accommodates imprecision, tolerance and uncertainty to achieve tractability, robustness and low cost. The fuzzy inference approach was employed to formulate the membership functions.Mamdani approach was used for the system design. The predisposing risk factors of breast cancer were elicited from four domain experts through direct contact this was used to generate the fuzzy rules. This study focuses on developing a Mobile-based Fuzzy Expert System (MFES) to predict an individual risk of initial cancer growth. ![]() However, there is need for early identification of the predisposing factors of the disease and its elimination. ![]() Previous studies using fuzzy logic were directed at reoccurrence/survivability. Currently, breast cancer is rated as the second leading cause of death among women. Fuzzy logic has different approaches for enhancing personal health care delivery.
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