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Mathematical Structure Of Fuzzy Modeling Of Medical Diagnoses By Using Clustering Models, R.W. W. Hndoosh
Mathematical Structure Of Fuzzy Modeling Of Medical Diagnoses By Using Clustering Models, R.W. W. Hndoosh
R. W. Hndoosh
An Adaptive-Network-based Fuzzy Inference System ANFIS with different techniques of clustering is successfully developed to solve one of the problems of medical diagnoses, because it has the advantage of powerful modeling ability. In this paper, we propose the generation of an adaptive neuro-Fuzzy Inference System model using different clustering models such as a subtractive fuzzy clustering (SFC) model and a fuzzy c-mean clustering (FCM) model in the Takagi-Sugeno (TS) fuzzy model for selecting the hidden node centers. An experimental result on datasets of medical diagnoses shows the proposed model with two models of clustering (ANFIS-SFC & ANFIS-FCM) while comparing the …
A Hybrid Prognostic Model For Oral Cancer Based On Clinicopathologic And Genomic Markers, Sameem Abdul Kareem
A Hybrid Prognostic Model For Oral Cancer Based On Clinicopathologic And Genomic Markers, Sameem Abdul Kareem
Sameem Abdul Kareem
There are very few prognostic studies that combine both clinicopathologic and genomic data. Most of the studies use only clinicopathologic factors without taking into consideration the tumour biology and molecular information, while some studies use genomic markers or microarray information only without the clinicopathologic parameters. Thus, these studies may not be able to prognoses a patient effectively. Previous studies have shown that prognosis results are more accurate when using both clinicopathologic and genomic data. The objectives of this research were to apply hybrid artificial intelligent techniques in the prognosis of oral cancer based on the correlation of clinicopathologic and genomic …