Health Care Decision Support System for Prediction of Multiple Diseases
B. Divya1, R. Senthil Kumar2
1B. Divya, UG Scholar, Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai (Tamil Nadu), India.
2Mr. R. Senthil Kumar, Assistant Professor (SG), Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai (Tamil Nadu), India.
Manuscript received on 26 April 2019 | Revised Manuscript received on 08 May 2019 | Manuscript Published on 17 May 2019 | PP: 463-467 | Volume-7 Issue-6S4 April 2019 | Retrieval Number: F10950476S419/2019©BEIESP
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: Data mining could be a fascinating field for analyzing data to find interesting patterns from large scale clinical medical datasets. Data exploration is a similar approach to perform data analysis, whereby a data analyst uses different visual exploration techniques to understand what is in a clinical dataset and the characteristics of data, rather than through traditional data management systems. The patient’s infection states will discover by formalizing the speculation visible of check outcomes and facet effects of the patients before proposing meds for the regular infections. The primary point of our paper is to construct a basic decision support system to assist specialists where one can determine and extract patterns, relations and concepts over multiple diseases from a large scale clinical datasets of multiple diseases. In this paper an efficient hierarchical clustering algorithm with proposed framework for prediction of a various diseases.
Keywords: Datamining, Prediction, Preprocessing, Clustering.
Scope of the Article: Regression and Prediction