[PDF][PDF] Performance Analysis and Optimization of Supervised Learning Techniques for Medical Diagnosis Using Open Source Tools
International Journal of Computer Science and Information Technologies, 2015•Citeseer
Data mining initiates the recent advances and applications in the promising areas of
medicine and biology around the world. In the medical diagnosis, it is difficult for the experts
to observe disease with assurance. By using data mining techniques, this problem can be
solved. This paper intends to provide the current techniques in data mining which are in use
in today's medical research particularly in Orthopaedic diagnosis. This paper presents a
comparative study of different classification techniques using two open source data mining …
medicine and biology around the world. In the medical diagnosis, it is difficult for the experts
to observe disease with assurance. By using data mining techniques, this problem can be
solved. This paper intends to provide the current techniques in data mining which are in use
in today's medical research particularly in Orthopaedic diagnosis. This paper presents a
comparative study of different classification techniques using two open source data mining …
Abstract
Data mining initiates the recent advances and applications in the promising areas of medicine and biology around the world. In the medical diagnosis, it is difficult for the experts to observe disease with assurance. By using data mining techniques, this problem can be solved. This paper intends to provide the current techniques in data mining which are in use in today’s medical research particularly in Orthopaedic diagnosis. This paper presents a comparative study of different classification techniques using two open source data mining tools named WEKA and TANAGRA. The aim of this paper is to analyze the performance of different classification techniques for a set of Orthopaedic (muscularskeletal) data.
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