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An AI-based Intelligent System for Healthcare Analysis Using Ridge–Adaline Stochastic Gradient Descent Classifier

Deepa, N, Prabadev, B, Kumar Reddy, PM, Thippa Reddy, G, Baker, T, Khan, AM and Tariq, U (2020) An AI-based Intelligent System for Healthcare Analysis Using Ridge–Adaline Stochastic Gradient Descent Classifier. Journal of Supercomputing. ISSN 0920-8542 (Accepted)

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Abstract

Recent technological advancements in information and communication technologies introduced smart ways of handling various aspects of life. Smart devices and applications are now an integral part of our daily life; however, the use of smart devices also introduced various physical and psychological health issues in modern societies. One of the most common health care issues prevalent among almost all age groups is diabetes mellitus. This work aims to propose an Artificial Intelligence (AI) – based intelligent system for earlier prediction of the disease using Ridge Adaline Stochastic Gradient Descent Classifier (RASGD). The proposed scheme RASGD improves the regularization of the classification model by using weight decay methods, namely Least Absolute Shrinkage and Selection Operator(LASSO) and Ridge Regression methods. To minimize the cost function of the classifier, the RASGD adopts an unconstrained optimization model. Further, to increase the convergence speed of the classifier, the Adaline Stochastic Gradient Descent classifier is integrated with Ridge Regression. Finally, to validate the effectiveness of the intelligent system, the results of the proposed scheme have been compared with state-of-art machine learning algorithms such as Support Vector Machine and Logistic Regression methods. The RASGD intelligent system attains an accuracy of 92%, which is better than the other selected classifiers.

Item Type: Article
Uncontrolled Keywords: 0803 Computer Software, 0805 Distributed Computing
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Computer Science & Mathematics
Publisher: Springer Verlag
Date Deposited: 21 May 2020 10:36
Last Modified: 21 May 2020 10:45
URI: https://researchonline.ljmu.ac.uk/id/eprint/12978

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