A Feature Selection Prediction Technique for Healthcare using Naive Bayes Algorithm
J. Betty Jane1, E.N. Ganesh2
1J. Bettyjane, Department of Computer Science and Engineering, Vels University/, Chennai, India.
2DR. E.N. Ganesh, Dean, School of Engineering, Vels University, Chennai, India.
Manuscript received on 05 April 2019 | Revised Manuscript received on 10 May 2019 | Manuscript published on 30 May 2019 | PP: 1467-1472 | Volume-8 Issue-1, May 2019 | Retrieval Number: A9263058119/19©BEIESP
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© 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: Nowadays, the data volume and its types and formats of data are very vast and complex in the field of health care. Bigdata is referred to as a huge volume of data that are complex to be handled with traditional database. The bigdata in healthcare plays an important role in enhancing treatments and facilities, hence the healthcare departments are in need to understand as much as they can, about the patient to prevent them from serious illness in the future. A feature selection technique is used for selecting subsets from large datasets and naive Bayes algorithm is used for classifying the datasets. The aim of the proposed work is to provide right information and accurate data to the organization, so that the data provided after predicting will enable the organization to ensure treating the patient’s illness which may occur in the future by the help of the results found by Feature selection classification technique. Then the classification is done through naïve bayes algorithm (NB) that analyse data and gives the prediction accuracy of the future outcome healthcare datasets.
Index Terms: Big Data, Naïve Bayes Theorem, Datasets, Feature Selection, Wrapper Method
Scope of the Article: Healthcare Informatics