Author(s): Meenakshi K, Safa M, Karthick T, Sivaranjani N

Email(s): meenakshi.k@ktr.srmuniv.ac.in

DOI: 10.5958/0974-360X.2017.00253.0   

Address: Meenakshi K*, Ms. Safa M, Mr. Karthick T, Ms. Sivaranjani N
Department of Information Technology, SRM University, Chennai, India
*Corresponding Author

Published In:   Volume - 10,      Issue - 5,     Year - 2017


ABSTRACT:
Machine learning is a kind of data analysis technique which provides a flexible way of learning information about the data, so that necessary action can be predicted accurately. Machine learning techniques provide the way of analyzing and predicting the valuable information from the available data, so that further actions can be carried out accurately. There is several kind of machine learning approaches are available based on their behavior and working procedure. In this analysis work different kind of methodologies are discussed which are used to learn the knowledge about the program. The various machine learning approaches differs in their working procedure and inputs and output processed by them. Various applications adapted the machine learning approaches for learning the information which are discussed in detail in this paper. The analysis work provides the detailed view of working procedure of different research works implemented by various authors. It also gives overview of merits and demerits of different machine learning techniques that are proposed earlier. The main goal of this analysis work is to identify the better machine learning approach which can lead to accurate learning with less false positive rate. The analysis of the work concluded with the performance results of different approaches that carried down and shows better approach which can lead to more accurate learning of health care data.


Cite this article:
Meenakshi K, Safa M, Karthick T, Sivaranjani N. A Novel Study of Machine Learning Algorithms for Classifying Health Care Data. Research J. Pharm. and Tech. 2017; 10(5): 1429-1432. doi: 10.5958/0974-360X.2017.00253.0


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RNI: CHHENG00387/33/1/2008-TC                     
DOI: 10.5958/0974-360X 

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