Performance Research on Medical Data Classification using Traditional and Soft Computing Techniques
Zahid Ansari1, Quazi Mateenuddin H.2, Ansari Abdullah3
1Zahid Ansari, Department of Computer Science and Engineering, P A College of Engineering Nadupadavu, Mangalore (Karnataka), India.
2Quazi Mateenuddin H., Faculty of Electronics and Communication Engineering, Indian Naval Acadamy, Ezhimala (Kerala), India.
3Ansari Abdullah, Department of Computer Science and Engineering, Bearys Institute of Technology, Mangalore (Karnataka), India.
Manuscript received on 22 July 2019 | Revised Manuscript received on 03 August 2019 | Manuscript Published on 10 August 2019 | PP: 990-995 | Volume-8 Issue-2S3 July 2019 | Retrieval Number: B11850782S319/2019©BEIESP | DOI: 10.35940/ijrte.B1185.0782S319
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The world today has made giant leaps in the field of Medicine. There is tremendous amount of researches being carried out in this field leading to new discoveries that is making a heavy impact on the mankind. Data being generated in this field is increasing enormously. A need has arisen to analyze these data in order to find out the meaningful and relevant hidden patterns. These patterns can be used for clinical diagnosis. Data mining is an efficient approach in discovering these patterns. Among the many data mining techniques that exists, this paper aims at analyzing the medical data using various Classification techniques. The classification techniques used in this study include k-Nearest neighbor (kNN), Decision Tree, Naive Bayes which are hard computing algorithms, whereas the soft computing algorithms used in this study include Support Vector Machine (SVM), Artificial Neural Networks (ANN) and Fuzzy k-Means clustering. We have applied these algorithms to three kinds of datasets that are Breast Cancer Wisconsin, Haberman Data and Contraceptive Method Choice dataset. Our results show that soft computing based classification algorithms better classifications than the traditional classification algorithms in terms of various classification performance measures.
Keywords: Medical Data Mining, Classification, Soft Computing Techniques.
Scope of the Article: Soft Computing