DEVELOPMENT AND IMPLEMENTATION OF AN INTELLIGENT SYSTEM FOR PREDICTING ALZHEIMER’S DISEASE
Loading...
Date
2025-07-02
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
University of Mohamed Boudiaf - M’sila
Abstract
Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder and the leading cause of
dementia, primarily affecting individuals over the age of 65. Patients experience a gradual decline
in cognitive functions, including memory, judgment, and functional abilities. The increasing prevalence
of AD underscores the urgent need for accurate and efficient computer-aided diagnosis (CAD)
methods. While most AD diagnostic studies focus on neuroimaging data, its high cost and limited
accessibility restrict its use to urban populations with advanced medical facilities, introducing potential
bias in machine learning (ML) models. Additionally, behavioral and psychological symptoms,
which are critical for AD diagnosis, are often overlooked. To address these gaps, this study investigates
two alternative diagnostic approaches—neuropsychological assessments and plasma protein
biomarkers—using ML techniques.
In the first approach, we evaluate the predictive performance of ML models using neuropsychological
assessment data collected through low-cost, first-line diagnostic tools. Both binary and
multiclass classification techniques are applied to analyze five neuropsychological assessments for
AD detection. The findings highlight the potential of neuropsychological data in early AD diagnosis
and emphasize the advantages of ML-based approaches in clinical decision-making.
In the second approach, we explore plasma protein biomarkers for AD diagnosis. We apply
Sequential Backward Feature Selection (SBFS) and Analysis of Variance (ANOVA) to extract significant
proteins from a dataset of 146 plasma proteins collected from 566 individuals, including
both AD patients and healthy controls. Five ML models—Decision Tree, Random Forest, Extremely
Randomized Trees, Extreme Gradient Boosting, and Adaptive Boosting—are used for initial classification,
followed by XGBoost and AdaBoost for final validation.
Both approaches demonstrate the feasibility of low-cost, accessible diagnostic methods for AD
detection. Neuropsychological assessments provide valuable cognitive and behavioral insights, while
plasma protein biomarkers offer a promising non-invasive alternative to traditional techniques.
These findings support the integration of ML-driven approaches in AD diagnosis, paving the way
for scalable and affordable screening strategies that can benefit a broader population
Description
Keywords
Alzheimer’s disease, ANOVA, Blood biomarker, Classification, Dementia, Feature selection, Machine learning, Neuropsychological assessment, Plasma proteins