Assessment for Alzheimer’s Disease Advancement Using Classification Models with Rules

• Published in: Applied sciences Vol. 13; no. 22; p. 12152
• Main Authors: Thabtah, Fadi, Peebles, David
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.11.2023
• Subjects: Accuracy; Advertising executives; Algorithms; Alzheimer's disease; Alzheimer’s disease (AD); Biomarkers; Classification; Cognition & reasoning; Data mining; Datasets; Dementia; Diagnostic imaging; Machine learning; Medical imaging; Medical research; Medicine, Experimental; Neuroimaging; neuropsychological assessments; Neuropsychology
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app132212152

Pre-diagnosis of common dementia conditions such as Alzheimer’s disease (AD) in the initial stages is crucial to help in early intervention, treatment plan design, disease management, and for providing quicker healthcare access. Current assessments are often stressful, invasive, and unavailable in most countries worldwide. In addition, many cognitive assessments are time-consuming and rarely cover all cognitive domains involved in dementia diagnosis. Therefore, the design and implementation of an intelligent method for dementia signs of progression from a few cognitive items in a manner that is accessible, easy, affordable, quick to perform, and does not require special and expensive resources is desirable. This paper investigates the issue of dementia progression by proposing a new classification algorithm called Alzheimer’s Disease Class Rules (AD-CR). The AD-CR algorithm learns models from the distinctive feature subsets that contain rules with low overlapping among their cognitive items yet are easily interpreted by clinicians during clinical assessment. An empirical evaluation of the Disease Neuroimaging Initiative data repository (ADNI) datasets shows that the AD-CR algorithm offers good performance (accuracy, sensitivity, etc.) when compared with other machine learning algorithms. The AD-CR algorithm was superior in comparison to the other algorithms overall since it reached a performance above 92%, 92.38% accuracy, 91.30% sensitivity, and 93.50% specificity when processing data subsets with cognitive and demographic attributes.