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Fuzzy logic has different approaches for enhancing personal health care delivery. Currently, breast cancer is rated as the second leading cause of death among women. Previous studies using fuzzy logic were directed at reoccurrence/survivability. However, there is need for early identification of the predisposing factors of the disease and its elimination. This study focuses on developing a Mobile-based Fuzzy Expert System (MFES) to predict an individual risk of initial cancer growth.
The predisposing Â risk factors of breast cancer were elicited from four domain experts through direct contact; this was used to generate the fuzzy rules. The fuzzy inference approach was employed to formulate the membership functions.Mamdani approach was used for the system design. The system accommodates imprecision, tolerance and uncertainty to achieve tractability, robustness and low cost.Â Java expert system shell running on Android operating system was used to achieve the mobile technology aspect. For the purpose of system evaluation, 2500 data were collected from two health care centers in Nigeria using random sampling.
The result indicated that the fact elicited from the experts served as range values for the 12 risk factors for Â fuzzification of the input and thus, 36 rules were generated. The rules were used for the system development. The developed MFES recorded 96% accuracy.
It is therefore recommended that MFES be used to detect breast cancer risk levels early enough. The main contribution of this work is to reduce the incidence rate in contrast to the existing methods currently applied in the diagnosis of breast cancer.
Keywords: Soft Computing, Fuzzy Set, Breast Cancer, Risk Factors, Membership Functions
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