A Mobile-Based Fuzzy Expert System for Breast Cancer Growth Prognosis
1.1 Background to the Study
Majority of the models applied in medical field are naturally unclear. As a result of the unclear (fuzzy) nature of medical data and models as well as the relationships that exist in the models, fuzzy logic technique is suitable for medical applications. Fuzzy logic (an aspect of soft computing) proposes approaches of result production that have the capability of estimated representation of decisions. As a result of the difficulty in medical exercise, the old-style numerical study methods are not satisfactory and may not be suitable. The utmost causes of ambiguity are as follows:
Incomplete data about an individual: either from patient or family members.
Often time, the patient’s state of health account is provided by the individual, or by the family member. These data to a large extent are subjective and ambiguous.
The well being check-up: Often time, medical practitioners get impartial facts.
Laboratory test and prognosis results may also be subject to various mistakes.
The delinquency of patient’s preceding health status check-up can also cause error in the test report.
Symptoms might be faked or overstated more/fewer than they truly appear.
Patients are likely to neglect some of the symptoms.
Some symptoms might be indescribable by patients.
Hence, fuzzy logic a soft computing methodology has the capability to reduce uncertainty in decision making in medical field.
1.2 Statement of the Problem
The most recurrent and second leading cause of death in women is breast cancer. The inadequacies of the existing methods, such as Mammography, Magnetic Resonance Imaging (MRI), Self-examination and others, account for the breast cancer high mortality. The shortcomings of the existing models include:
Late discovery of the cancerous germs – these methods only detect breast cancer at the metastatic stage. (the tumour has grown and spread to other parts of the body);
Existing models cause patients pains and related inconvenience which dissuade women from voluntary screening. Thus, most people do not report cases of breast cancer until it has got to the third stage and stack the odd of survival against the patient.
Imprecise diagnosis because it involves several layers of uncertainty. These shortcomings make the traditional approaches inappropriate.
Thousands of people fall victim to breast cancer every year due to limitation of medical services and the inability to use the existing services effectively. Late presentation of cases at advanced stages when little or no benefit can be derived from any form of therapy is the hallmark of breast cancer among Nigerian women. The available breast cancer calculators are only focused on survivability and re-occurrence and also not safe because individuals do not know where their personal data is being saved. To curtail the worsening incidence of breast cancer deaths, a Mobile-based Fuzzy Expert System (MFES) for breast cancer pre-growth prognosis that would obviate the inadequacies of the existing models, encourage voluntary personal screening and more importantly, detect the risk of developing breast cancer is designed. Pre growth prognosis of a disease like breast cancer is very crucial to a successful reduction of death rate caused by the disease. This research weaved its solution/prognosis intervention around a nature motivated method that is biologically inspired. This method would be able to detect the risk of early developments and proffered likely solutions thereby reducing the consequence of ignorance which may lead to death.
1.3 Objective of the Study
The general objective of this study work is to design and implement a Mobile-based Fuzzy Expert System (MFES) for breast cancer pre-growth prognosis. The fuzzy expert system would be capable of capturing ambiguous and imprecise information prevalent in breast cancer prognosis. The specific objectives are to:
determine the range values for the Membership Function (BreastCancerRisk factors) using experts rating for the indicators for fuzzification;
formulate the membership functions using information in (1);
design a MFES for breast cancer pre-growth prognosis and implement and carry out performance evaluation of the developed mobile based fuzzy expert system using in comparison existing fuzzy logic models.
In order to achieve the stated objectives, the following approaches were considered:
1. Upper and lower values were determined from the values (facts) collected from the domain experts to determine the membership functions.
2. Membership functions for all the risk factors were formulated, using the values in (1).
3. The rules for all the risk factors were formulated.
4. Java expert system shell (JESS) was used to develop the MFES, using the informations in (1), (2) and (3) and this runs on Android systems.
5. The MFES performance evaluation was carried out using data from healthy people and those already diagnosed with the disease and also in comparison with existing fuzzy logic models.
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