Main Article Content
The death rate is caused by breast cancer in women is increasingly high and growing. A number of people are getting to lose this part of their body due to late diagnosis of this disease. This therefore requires the development of an efficient and accurate diagnosis approach that will aid providing the knowledge of the type of breast cancer type and severity in order to reduce the mortality rate through the disease. This need serves as the major motivation for this work. In this paper, we proposed a fuzzy expert system for diagnosis of and treatment recommendation of breast cancer problems which provide physicians and patients with information of the cancer type and treatment recommendation. The application was designed using JAVA programming language, MATLAB and SQLite database engine. This application permits update of new information as a means of knowledge. The evaluation showed that the inclusion of the fuzzy inference system improved the accuracy and precision of the system from 0.8 to 0.9. The system is user-friendly and has high level of acceptability from the validation conducted at the end of the research.
Boughorbel S, Al-Ali R, Elkum N. Model comparison for breast cancer prognosis based on clinical data. PloS One. 2016;11(1):e0146413.
(Accessed February, 2018)
Bhardwaj A, Tiwari A. Breast cancer diagnosis using genetically optimized neural network model. Expert Systems with Applications. 2015;42(10):4611-4620.
Daliri MR. A hybrid automatic system for the diagnosis of lung cancer based on genetic algorithm and fuzzy extreme learning machines. Journal of Medical Systems. 2012;36(2):1001-1005.
Polat K, Güneş S. Principles component analysis, fuzzy weighting pre-processing and artificial immune recognition system based diagnostic system for diagnosis of lung cancer. Expert Systems with Applications. 2008;34(1):214-221.
Alzubaidi A, Cosma G, Brown D, Pockley AG. Breast cancer diagnosis using a hybrid genetic algorithm for feature selection based on mutual information. In Interactive Technologies and Games (iTAG), 2016 International Conference on IEEE. 2016;70-76.
Feng F, Wu Y, Wu Y, Nie G, Ni R. The effect of artificial neural network model combined with six tumor markers in auxiliary diagnosis of lung cancer. Journal of Medical Systems. 2012;36(5):2973-2980.
Lu C, Zhu Z, Gu X. An intelligent system for lung cancer diagnosis using a new genetic algorithm based feature selection method. Journal of Medical Systems. 2014;38(9):97.
Chen HL, Yang B, Liu J, Liu DY. A support vector machine classifier with rough set-based feature selection for breast cancer diagnosis. Expert Systems with Applications. 2011;38(7):9014-9022.
Zheng B, Yoon SW, Lam SS. Breast cancer diagnosis based on feature extraction using a hybrid of K-means and support vector machine algorithms. Expert Systems with Applications. 2014;41(4): 1476-1482.
Hassanien AE, Moftah HM, Azar AT, Shoman M. MRI breast cancer diagnosis hybrid approach using adaptive ant-based segmentation and multilayer perceptron neural networks classifier. Applied Soft Computing. 2014;14:62-71.
Venkatesh V, Bala H. Technology acceptance model 3 and a research agenda on interventions. Decision Sciences. 2008;39:273–315.
Abdullah NS, Indulska M, Sadiq S. Compliance management ontology–a shared conceptualization for research and practice in compliance management. Information Systems Frontiers. 2016;1-26.