Work place: School of Engineering, Department of Computer Science and Engineering, Kathmandu University, Dhulikhel, Kavre, Nepal
E-mail: gajendra.sharma@ku.edu.np
Website:
Research Interests: Database Management System, Information Systems, Software Creation and Management
Biography
Gajendra Sharma completed doctoral degree in Information Systems Engineering from Harbin Institute of Technology, China. He received the degree of Masters of Engineering in Electronics and Communication in 1997 from Moscow Technical University of Telecommunication and Informatics, Russia. His research and teaching interest is focused on information systems, e-commerce (including e-business), strategic management of information technology (IT), IT adoption, design and evaluation of IT infrastructure, strategic management of IT as well as e-governance and ethics. He published research papers in some of the top-tier information systems and IT journals such as Information Systems Frontiers, Internet Research, Information Technology and People, Telecommunications Policy, International Journal of Web Based Communities and Electronic Commerce Research. He is a reviewer and technical editor of a number of peer review journals relating to information systems and IT. He worked in Liaoning Technical University, China at the department of Information Systems as an Associate Professor from 2011-2014. He completed postdoctoral research on technology philosophy (e-government and ethics) from Dalian University of Technology, China coordinating with Delft University of Technology, Netherlands. In the meantime, he has been working in Kathmandu University, Nepal.
DOI: https://doi.org/10.5815/ijeme.2020.02.04, Pub. Date: 8 Apr. 2020
With the development of technology the use of Computer Aided Diagnosis has become a key for breast cancer diagnosis. It is important to increase the accuracy and effective of such systems. The concept of data mining can be applied on the data gathered through such systems for prediction and prevention of breast cancer. In this research, we have conducted the comparison between seven classification algorithms with the help of WEKA (The Waikato Environment for Knowledge Analysis) tool on the 569 instances (10 nucleus attributes) of data with two classes Malignant(M) and Benign (B) of breast cancer aspirate cells. Furthermore the influence of each attribute on prediction was evaluated. The accuracy of these algorithms was above 91% with the highest value of 94.02% for random forest and the predictive power of conclave points was highest whereas lowest was of Fractal Dimension.
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