An Integrated Fuzzy Model For Pattern Recognition - Sagir, Abdu Masanawa (2017)
Medical diagnosis is a process of investigating which medical condition, disease or disorder describes signs and symptoms of a patient. Medical diagnosis helps to obtain different features representing the different variation of the disease. The decision about presence or absence of diseases of patients is a challenging task because many signs and symptoms are non-specific; and many tests might be required. To recognise an accurate diagnosis of symptom analysis, the physician may need efficient diagnosis system that can predict and classify patient condition. This thesis describes a methodology for developing an integrated fuzzy model by utilising the application of adaptive neuro fuzzy inference system (ANFIS) that can be used by physicians to accelerate diagnosis process. Feature selection approach was used to identify and remove unneeded, irrelevant and redundant attributes from the data that do not contribute to the accuracy of a predictive model. The proposed method used Hold-out validation technique, which divides the training and test data sets into twothirds to one-third, respectively. The proposed method uses grid partition technique to cope with seven input attributes and Gaussian membership functions than conventional method built-in Matlab, which uses small number of input attributes usually less than five. For robustness, twelve benchmarked datasets obtained from University of California at Irvine’s (UCI) machine learning repository were used in this research.