ABSTRACT
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 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. 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 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.
CHAPTER ONE
INTRODUCTION
Background to the Study Information and Communication Technology (ICT), specifically mobile health (mHealth), can play a key role in enhancing and enabling health care systems, when linked to specific needs. The initiation of various types of mobile portable computer devices – smartphones, private digitally powered assistants, and tablet systems has influenced an appreciated positive impact in many works of life which includes the health sector. This has been influenced by the increasing excellence and availability of application software in the health sector, (Aungst, 2013). These softwares are set of instructions that have been written in a particular programming language to run on a moveable portable aid or on a computer system to achieve a particular purpose, (Wallace, Clark & White, 2012). In recent development faster processors and improved memory in the analysis of complex data in the health sector have paved the way for diverse medical mobile expert systems. These systems are either individualised or used by medical expert (Ozdalga, Ozdalga& Ahuja, 2012). These portable application systems are designed to supplement the experts work in order to deliver a resource that will advance the results for private health monitoring and at the point of care (Aungst, 2013). There are existing medical expert system models and health calculators which include Breast Cancer Surveillance Consortium (BCSC) Risk Calculator (for breast cancer risk calculation), the Breast Cancer Risk Assessment Tool (the Gail model) often used by health care providers to estimate risk, MedCalc. These models did not explore detail risk factors for breast cancer growth, and detail fuzzy rules were not
