Comparative Analysis of Prediction Algorithms for Diabetes

Karun, Shweta and Raj, Aishwarya and Attigeri, Girija V (2018) Comparative Analysis of Prediction Algorithms for Diabetes. In: Advances in Computer Communication and Computational Sciences. Springer, Singapore.

[img] PDF
154.pdf - Published Version
Restricted to Registered users only

Download (497kB) | Request a copy


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

Item Type: Book Section
Uncontrolled Keywords: Machine learning Predictive algorithm Feature extraction Diabetes prediction Classification Ensemble
Subjects: Engineering > MIT Manipal > Information and Communication Technology
Depositing User: MIT Library
Date Deposited: 19 Sep 2019 06:28
Last Modified: 19 Sep 2019 06:28

Actions (login required)

View Item View Item