An Exploration of Various Data Mining Techniques for Application in Child Healthcare
Abhijeet Sudhakar1, Rajendra B. Patil2, Srivaramangai R3
1Abhijeet Sudhakar, Student, University of Mumbai, Mumbai, India.
2Rajendra B. Patil, Assistant Professor, S.K.Somaiya College of Arts, Science & Commerce, Mumbai, India.
3Srivaramangai R, Assistant Professor, Department of Information Technology, University of Mumbai, Mumbai, India.
Manuscript received on 17 August 2019. | Revised Manuscript received on 24 August 2019. | Manuscript published on 30 September 2019. | PP: 8293-8296 | Volume-8 Issue-3 September 2019 | Retrieval Number: C6591098319/2019©BEIESP | DOI: 10.35940/ijrte.C6591.098319
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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: Data mining has been predominantly used for analysis of various data and findings. It has already gained ample importance in the field of medicine especially the healthcare department of governments. It is a useful tool for analyzing the child health care data for government so that enough measures can be taken to reduce the causes of deaths. This paper aims at exploring different data mining techniques that have been implemented especially in the child healthcare sector in order to get analytical data for decision making. Various techniques have been used for finding the mortality rates among infants, the under nutrition percentage, the causes of different diseases among children of under 5 years age. This paper gives an analysis of the earlier research and presents the need for developing new algorithms for healthcare with more accuracy in predictions.
Keywords: Data Mining, Healthcare, Data Analytics, Artificial Neural Networks, Naïve Bayes Classifier, Support Vector Machine, K-Nearest Neighbour
Scope of the Article: Healthcare Informatics