Efficient Classification of Medical Data and Disease Prediction Using Multi Attribute Disease Probability Measure

Authors

K. Ananthajothi, Department of Computer Science and Engineering, Misrimal Navajee Munoth Jain Engineering College,Chennai, India.Follow
M. Subramaniam, S.A.Engineering College, Chennai, India.Follow

Author Country (or Countries)

India.

Abstract

In this paper, we introduce a multi attribute disease probability -based classification approach. The method first identifies the list of features available in the data set. Based on identified features, the data points have been verified for their completeness with all the features identified. The data points having incomplete and missing features have been eliminated from the data set. Further the method computes the probability measure on each dimension. According to the probability measure of various dimensions, the multi attribute disease probability (MADP) has been measured for each disease class. It is shown that the proposed MADP -based disease prediction algorithm achieves higher classification ratio and disease prediction accuracy.

Digital Object Identifier (DOI)

http://dx.doi.org/10.18576/amis/130511

Recommended Citation

Ananthajothi, K. and Subramaniam, M. (2019) "Efficient Classification of Medical Data and Disease Prediction Using Multi Attribute Disease Probability Measure," Applied Mathematics & Information Sciences: Vol. 13: Iss. 5, Article 11.
DOI: http://dx.doi.org/10.18576/amis/130511
Available at: https://digitalcommons.aaru.edu.jo/amis/vol13/iss5/11