Title: A filter-based machine learning classification framework for cloud-based medical databases

Authors: V. Devi Satya Sri; Srikanth Vemuru

Addresses: Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur District, AP, India ' Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur District, AP, India

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.

Keywords: medical databases; machine learning; cloud computing.

DOI: 10.1504/IJAHUC.2022.10048194

International Journal of Ad Hoc and Ubiquitous Computing, 2022 Vol.40 No.1/2/3, pp.94 - 105

Received: 25 Jan 2021
Accepted: 03 Mar 2021

Published online: 27 Jun 2022 *

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