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Title: Hybrid Multistage Fuzzy Clustering System for Medical Data Classification
Authors: Maryam A. Abdullah 
Supervisor: Prof. Fawaz S. Al-Anzi
Keywords: Hybrid Multistage : Clustering System
Issue Date: 2017
Publisher:  Kuwait university - college of graduate studies
Abstract: Due to the rapid development in technology nowadays, massive amount of data are available. In medicine, decision making is entirely based on the hidden information in these massive data. For that reason, data mining and machine learning technologies provide powerful tools for knowledge discovery within data. Two main techniques are used interchangeably: clustering and classification. In machine learning, clustering is an unsupervised learning technique while classification is a supervised learning method. These techniques are capable of extracting useful patterns and information which aid the process of data analysis and clinical decisions. This thesis presents an overview of the most common algorithms and their advancements in classification and clustering field in addition to a recent study of these techniques in the medical field during the past five years. These methods were used in a variety of medical applications such as disease detection, disease diagnosis, and medical image segmentation. Moreover, this thesis proposes a hybrid multistage fuzzy clustering system applied to medical data classification. In the proposed system, two fuzzy clustering algorithms specifically FCM and GK were initially employed to obtain the membership values of every instance in the dataset to which they belong to every class. These weights are then used in the second stage of the system as additional informative features to improve the classification process completed by SVM algorithm. Wisconsin Breast Cancer dataset, real-world application, obtained from UCI were used in the experiments. The results of the experiments show that the additional weights further improve the classification accuracy with 99.06% and 100% sensitivity.
Appears in Programs:0612 Computer Engineering

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