Open Access

ARTICLE

Improved Metaheuristics with Machine Learning Enabled Medical Decision Support System

by Sara A. Althubiti¹, José Escorcia-Gutierrez^2,3,*, Margarita Gamarra⁴, Roosvel Soto-Diaz⁵, Romany F. Mansour⁶, Fayadh Alenezi⁷

1 Department of Computer Science, College of Computer and Information Sciences, Majmaah University, Al-Majmaah, 11952, Saudi Arabia
2 Research Center - CIENS, Escuela Naval de Suboficiales A.R.C. “Barranquilla”, Barranquilla, Colombia
3 Electronics and Telecommunications Engineering Program, Universidad Autónoma del Caribe, Barranquilla, 080001, Colombia
4 Departament of Computational Science and Electronic, Universidad de la Costa, CUC, Barranquilla, 080001, Colombia
5 Biomedical Engineering Program, Universidad Simón Bolívar, Barranquilla, 080001, Colombia
6 Department of Mathematics, Faculty of Science, New Valley University, El-Kharga 72511, Egypt
7 Department of Electrical Engineering, College of Engineering, Jouf University, Saudi Arabia

* Corresponding Author: José Escorcia-Gutierrez. Email:

Computers, Materials & Continua 2022, 73(2), 2423-2439. https://doi.org/10.32604/cmc.2022.028878

Received 20 February 2022; Accepted 02 April 2022; Issue published 16 June 2022

Abstract

Smart healthcare has become a hot research topic due to the contemporary developments of Internet of Things (IoT), sensor technologies, cloud computing, and others. Besides, the latest advances of Artificial Intelligence (AI) tools find helpful for decision-making in innovative healthcare to diagnose several diseases. Ovarian Cancer (OC) is a kind of cancer that affects women’s ovaries, and it is tedious to identify OC at the primary stages with a high mortality rate. The OC data produced by the Internet of Medical Things (IoMT) devices can be utilized to differentiate OC. In this aspect, this paper introduces a new quantum black widow optimization with a machine learning-enabled decision support system (QBWO-MLDSS) for smart healthcare. The primary intention of the QBWO-MLDSS technique is to detect and categorize the OC rapidly and accurately. Besides, the QBWO-MLDSS model involves a Z-score normalization approach to pre-process the data. In addition, the QBWO-MLDSS technique derives a QBWO algorithm as a feature selection to derive optimum feature subsets. Moreover, symbiotic organisms search (SOS) with extreme learning machine (ELM) model is applied as a classifier for the detection and classification of ELM model, thereby improving the overall classification performance. The design of QBWO and SOS for OC detection and classification in the smart healthcare environment shows the study’s novelty. The experimental result analysis of the QBWO-MLDSS model is conducted using a benchmark dataset, and the comparative results reported the enhanced outcomes of the QBWO-MLDSS model over the recent approaches.

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Keywords

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