Browsing by Author "Zakaria MOKADEM"

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    DEVELOPMENT AND IMPLEMENTATION OF AN INTELLIGENT SYSTEM FOR PREDICTING ALZHEIMER’S DISEASE
    (University of Mohamed Boudiaf - M’sila, 2025-07-02) Zakaria MOKADEM; enca/DJERIOUI Mohamed
    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