TY - GEN
T1 - Comparative analysis of prediction algorithms for diabetes
AU - Karun, Shweta
AU - Raj, Aishwarya
AU - Attigeri, Girija
PY - 2019/1/1
Y1 - 2019/1/1
N2 - Machine learning is a widely growing field which helps in better learning from data and its analysis without any human intervention. It is being popularly used in the field of healthcare for analyzing and detecting serious and complex conditions. Diabetes is one such condition that heavily affects the entire system. In this paper, application of intelligent machine learning algorithms like logistic regression, naïve Bayes, support vector machine, decision tree, k-nearest neighbors, neural network, and random decision forest are used along with feature extraction. The accuracy of each algorithm, with and without feature extraction, leads to a comparative study of these predictive models. Therefore, a list of algorithms that works better with feature extraction and another that works better without it is obtained. These results can be used further for better prediction and diagnosis of diabetes.
AB - Machine learning is a widely growing field which helps in better learning from data and its analysis without any human intervention. It is being popularly used in the field of healthcare for analyzing and detecting serious and complex conditions. Diabetes is one such condition that heavily affects the entire system. In this paper, application of intelligent machine learning algorithms like logistic regression, naïve Bayes, support vector machine, decision tree, k-nearest neighbors, neural network, and random decision forest are used along with feature extraction. The accuracy of each algorithm, with and without feature extraction, leads to a comparative study of these predictive models. Therefore, a list of algorithms that works better with feature extraction and another that works better without it is obtained. These results can be used further for better prediction and diagnosis of diabetes.
UR - http://www.scopus.com/inward/record.url?scp=85053249043&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85053249043&partnerID=8YFLogxK
U2 - 10.1007/978-981-13-0341-8_16
DO - 10.1007/978-981-13-0341-8_16
M3 - Conference contribution
AN - SCOPUS:85053249043
SN - 9789811303401
T3 - Advances in Intelligent Systems and Computing
SP - 177
EP - 187
BT - Advances in Computer Communication and Computational Sciences - Proceedings of IC4S 2017
A2 - Bhatia, Sanjiv K.
A2 - Tiwari, Shailesh
A2 - Trivedi, Munesh C.
A2 - Mishra, Krishn K.
PB - Springer Verlag
T2 - International Conference on Computer, Communication and Computational Sciences, IC4S 2017
Y2 - 11 October 2017 through 12 October 2017
ER -
