Quantum Grey Wolf optimisation and evolutionary algorithms for diagnosis of Alzheimer's disease. (14th November 2022)

• Record Type: Journal Article
• Authors: Sharma, Moolchand; Gupta, Shubbham; Aggarwal, Himanshu; Aggarwal, Tarun; Gupta, Deepak; Khanna, Ashish
• Is Part Of: International journal of modelling, identification and control. Volume 41:Number 1/2(2022)
• Page Start: 53
• Page End: 67
• Publication Date: 2022-11-14
• Subjects: Alzheimer's disease -- machine learning -- neurodegenerative disease -- bio-inspired algorithms -- quantum Grey Wolf optimisation -- qGWO; Engineering -- Methodology -- Periodicals; Science -- Methodology -- Periodicals; 001.42
• Languages: English
• ISSNs: 1746-6172

Abstract

Alzheimer's disease (AD) is a type of brain cancer, similar to coronary artery disease. AD is a progressive neurological disorder that impairs memory, thinking abilities, and behaviour. Thus, early detection of the condition is critical, as there is no cure. We conducted a comparative analysis of various evolutionary algorithms for extracting meaningful information from the Alzheimer's dataset, which is then utilised to predict whether or not a patient has the illness. We attained an accuracy of 78%-85% using machine learning methods. When we utilised various evolutionary algorithms to perform feature selection, we observed an increase in accuracy of 5%-10%, with Grey Wolf and quantum Grey Wolf optimisation (qGWO) achieving highest accuracy of 92.8% and 94.5%, respectively, using random forest classifier. The model was evaluated using three metrics: the increase in accuracy, the time required to execute, and the number of features eliminated. Additionally, the testing revealed that certain characteristics are replicated across multiple models and might be regarded critical in the process of identifying AD.