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A mobile-based fuzzy expert system for breast cancer growth prognosis



Abstract Studies show that fuzzy logic has different approaches to enhancing personal health care delivery in the health care sector. Currently, breast cancer rated as the second leading cause of death among women, according to the World Health Organisation. Previous studies relating to breast cancer prognosis using a fuzzy logic approach were directed at the reoccurrence of the disease as well as the survivability of individuals. However, there is a need for early identification of the predisposing risk factors to breast cancer growth and its elimination. Consequently, this study focuses on developing an efficient Mobile-based Fuzzy Expert System (MFES) for initial breast cancer growth prognosis that can predict the individual risk level and thus, reduce the high incidence rate. Facts relating to the predisposing factors of breast cancer were elicited from four domain experts through direct contact this was used to generate the appropriate fuzzy rules. The fuzzy inference approach was employed to formulate the membership functions and fuzzy rules to design the MFES. Mamdani approach was used for the fuzzification of input and de-fuzzification of the output. The system accommodates imprecision tolerance and uncertainty to achieve tractability, robustness and least solution cost.  Java expert system shell running on the Android operating system was used to achieve the mobile technology aspect of the system. For the purpose of system evaluation, 2500 data were collected from two health care centers in Nigeria using a random sampling technique. The data were stratified into twenty-five different strata. Each stratum contained 100 dataset and four individual data were selected at random.


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