A filter-based machine learning classification framework for cloud-based medical databases Online publication date: Mon, 27-Jun-2022
by V. Devi Satya Sri; Srikanth Vemuru
International Journal of Ad Hoc and Ubiquitous Computing (IJAHUC), Vol. 40, No. 1/2/3, 2022
Abstract: Machine learning tools and techniques play a vital role in the medical field and cloud computing applications. Most of the traditional machine learning models use static metrics, limited data size and limited feature space due to high computational processing time. In this work, a hybrid outlier detection and data transformation approaches are implemented on the cloud-based medical databases. Proposed data filtering module is applicable to high dimensional data size and feature space for classification problem. In the classification problem, an advanced boosting classifier is implemented on the filtered data in order to improve the true positive and error rate. Experimental results are simulated on different medical datasets such as tonsil and trauma databases with different feature space size and data size. Simulation results proved that the proposed boosting classifier has better error rate and statistical accuracy than the conventional approaches.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Ad Hoc and Ubiquitous Computing (IJAHUC):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook