Open Access

ARTICLE

Mathematical Modelling of Quantum Kernel Method for Biomedical Data Analysis

Mahmoud Ragab^1,2,3, Ehab Bahauden Ashary⁴, Maha Farouk S. Sabir⁵, Adel A. Bahaddad⁵, Romany F. Mansour^6,*
1 Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
2 Centre of Artificial Intelligence for Precision Medicines, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
3 Mathematics Department, Faculty of Science, Al-Azhar University, Naser City, 11884, Cairo, Egypt
4 Electrical and Computer Engineering Department, Faculty of Engineering, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
5 Information Systems Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
6 Mathematics Department, Faculty of Science, New Valley University, El-Kharga, 72511, Egypt
* Corresponding Author: Romany F. Mansour. Email:

Computers, Materials & Continua 2022, 71(3), 5441-5457. https://doi.org/10.32604/cmc.2022.024545

Received 21 October 2021; Accepted 03 December 2021; Issue published 14 January 2022

Abstract

This study presents a novel method to detect the medical application based on Quantum Computing (QC) and a few Machine Learning (ML) systems. QC has a primary advantage i.e., it uses the impact of quantum parallelism to provide the consequences of prime factorization issue in a matter of seconds. So, this model is suggested for medical application only by recent researchers. A novel strategy i.e., Quantum Kernel Method (QKM) is proposed in this paper for data prediction. In this QKM process, Linear Tunicate Swarm Algorithm (LTSA), the optimization technique is used to calculate the loss function initially and is aimed at medical data. The output of optimization is either 0 or 1 i.e., odd or even in QC. From this output value, the data is identified according to the class. Meanwhile, the method also reduces time, saves cost and improves the efficiency by feature selection process i.e., Filter method. After the features are extracted, QKM is deployed as a classification model, while the loss function is minimized by LTSA. The motivation of the minimal objective is to remain faster. However, some computations can be performed more efficiently by the proposed model. In testing, the test data was evaluated by minimal loss function. The outcomes were assessed in terms of accuracy, computational time, and so on. For this, databases like Lymphography, Dermatology, and Arrhythmia were used.

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Keywords

Medical data classification; feature selection; qkm classifier; ltsa optimization

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