Qualification: 
M.Tech
k_sangita@blr.amrita.edu

Sangita Khare currently serves as Assistant Professor at department of Computer Science,Amrita School of Engineering.

Publications

Publication Type: Conference Paper

Year of Publication Publication Type Title

2017

Conference Paper

A. Nair, Dr. Deepa Gupta, Sangita Khare, Gopalkrishna, D. Manippady, and Dr. Amalendu Jyotishi, “Characteristics and causes of malnutrition across Indian states: A cluster analysis based on Indian demographic and health survey data”, in International Conference on Advances in Computing, Communications & Informatics (ICACCI’17), Manipal University, Karnataka , 2017.

2017

Conference Paper

Sangita Khare, Dr. Deepa Gupta, K, P., Dr. Deepika M. G., and Dr. Amalendu Jyotishi, “Health and Nutritional Status of Children: Survey, Challenges and Directions”, in 3rd International Conference on Cognitive Computing and information Processing (CCIP 17), JSSATE-B Campus, Bengaluru , 2017.

2015

Conference Paper

Va Dominic, Dr. Deepa Gupta, Sangita Khare, and Aggarwal, Ab, “Investigation of chronic disease correlation using data mining techniques”, in 2015 2nd International Conference on Recent Advances in Engineering and Computational Sciences, RAECS 2015, 2015.[Abstract]


A disease is an abnormal condition that affects the structure and function of one or more parts of the body. It may be caused by various factors, external and internal dysfunctions. There is a trend of various chronic diseases in any society. The major concern is that these chronic diseases are leading to many other diseases in future. An attempt to explore the correlation of various chronic diseases has become a necessity. This can be achieved by using data mining techniques, which help to derive knowledge about the affects of a particular chronic disease on the other chronic diseases. Since there is growing trend of diabetes and ischemic heart disease in the society, in this paper the focus is to investigate the effect of these diseases on the other chronic diseases using the ICD9 diagnostic codes. To achieve this goal various types of data mining techniques are used. The conclusion is an optimal set of ICD9 diagnostic codes associated with individuals having diabetes or ischemic heart disease. These codes are then investigated based on the human anatomic systems i.e. Circulatory system, Respiratory system, Nervous system, Musculoskeletal system, Renal system and Neoplasm and their relevance is justified. © 2015 IEEE.

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Faculty Research Interest: 
207
PROGRAMS
OFFERED
5
AMRITA
CAMPUSES
15
CONSTITUENT
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A
GRADE BY
NAAC, MHRD
8th
RANK(INDIA):
NIRF 2018
150+
INTERNATIONAL
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