A Classification Approach for instant Medical Assistance to Health Seekers

Authors(2) :-Pritha Tikariha, Prashant Richhariya

Data mining approach is applied in numerous numbers of fields for predicting and forecasting the events. However, in healthcare sectors, due to lack of faith in prediction method people hesitate to utilize data mining technique for health issues. People post their health related queries and get reply from the experts in many online healthcare applications. However, health seekers do not get instant assistance there; they need to wait for the experts for their opinion. Many data is accumulated in repository of such application. Using data mining techniques, useful information can be extracted from such repository, which can help health seekers to get instant assistance for their health related issues. These paper presents analysis on some data mining technique particularly in disease dataset. Three classification algorithms i.e. KNN, SVM, Naïve Bayes are applied on three disease dataset, to analyse the performance of the classifiers. The predictive rate is evaluated using four evaluation parameters i.e. Accuracy, precision, recall and f_measure. The experiment is performed in Matlab tool shows that Naïve Bayes outperforms as compared to rest of the classifiers.

Authors and Affiliations

Pritha Tikariha
Department of Computer Science, CSIT, Durg, Chhatishgarh, India
Prashant Richhariya
Department of Computer Science, CSIT, Durg, Chhatishgarh, India


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Publication Details

Published in : Volume 2 | Issue 3 | May-June 2017
Date of Publication : 2017-06-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 872-875
Manuscript Number : CSEIT1723320
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Pritha Tikariha, Prashant Richhariya, "A Classification Approach for instant Medical Assistance to Health Seekers", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 3, pp.872-875, May-June-2017.
Journal URL : http://ijsrcseit.com/CSEIT1723320

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