Sagir, Abdu Masanawa
(2017)
An Integrated Fuzzy Model For
Pattern Recognition.
PhD thesis, Perpustakaan Hamzah Sendut.
Abstract
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.
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