A Machine Learning Framework for Early Alzheimer's Detection Using Cognitive Scores and MRI-Based Image Feature Analysis

Alzheimer's Disease (AD) represents one of the most prevalent and devastating neurodegenerative disorders, posing profound challenges to public health systems worldwide due to its progressive nature and the lack of curative treatments. Early detection remains critical for managing disease progression, enabling timely therapeutic interventions, and improving patient outcomes. This study proposes a robust machine learning (ML) framework for the early detection of Alzheimer's Disease through the integrative analysis of cognitive assessment scores and magnetic resonance imaging (MRI)-based image features. The framework utilizes a multimodal dataset comprising neuropsychological test results and high-resolution structural MRI scans sourced from publicly available cohorts, including the Alzheimer's Disease Neuroimaging Initiative (ADNI). Cognitive metrics such as the Mini-Mental State Examination (MMSE), Clinical Dementia Rating (CDR), and Alzheimer’s Disease Assessment Scale–Cognitive Subscale (ADAS-Cog) are combined with extracted MRI-based volumetric and morphometric features—particularly from brain regions vulnerable to AD-related atrophy (e.g., hippocampus, entorhinal cortex). Feature engineering techniques, including principal component analysis (PCA) and mutual information ranking, are applied to reduce dimensionality and highlight salient biomarkers. Multiple supervised machine learning algorithms—namely Support Vector Machines (SVM), Random Forests, Gradient Boosting, and deep neural networks—are trained and validated on stratified datasets to distinguish between cognitively normal individuals, patients with mild cognitive impairment (MCI), and those with early-stage Alzheimer’s. Evaluation metrics such as accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (AUC-ROC) are used to assess diagnostic performance. The best-performing models achieved classification accuracies exceeding 90%, with MRI features contributing significantly to early MCI detection when fused with cognitive data. Additionally, SHAP (Shapley Additive Explanations) and Grad-CAM techniques are integrated to ensure model transparency and interpretability, facilitating clinical trust in AI-based diagnostics. The findings underscore the efficacy of a hybrid data-driven approach in enhancing the sensitivity and specificity of Alzheimer’s screening tools. This research contributes to the growing body of literature advocating for AI-enhanced clinical decision support systems in neurology and demonstrates that machine learning models, grounded in multimodal data fusion and explainability, can play a pivotal role in addressing the complex challenge of early Alzheimer's detection. CHAPTER ONE: INTRODUCTION 1.1 Background of the Study Alzheimer’s Disease (AD) is a progressive and irreversible neurodegenerative disorder that primarily affects the elderly population and leads to cognitive impairment, memory loss, language deterioration, and eventually complete functional dependence. As the global population ages, the prevalence of AD is expected to rise dramatically, posing significant social, economic, and healthcare challenges. According to the World Health Organization, over 55 million people currently live with dementia globally, with Alzheimer’s Disease accounting for 60–70% of these cases. The societal cost of managing Alzheimer’s and related dementias is projected to exceed one trillion dollars globally, thereby intensifying the need for early detection and intervention. Early diagnosis of Alzheimer’s Disease is crucial, as it enables patients to receive appropriate therapeutic interventions, participate in clinical trials, and plan for the future. However, traditional diagnostic methods, including clinical interviews, neuropsychological tests, and radiological assessments, are often subjective, time-consuming, and sometimes inconclusive, especially in the prodromal stages. Structural imaging techniques, such as magnetic resonance imaging (MRI), have provided valuable insights into neuroanatomical changes, particularly in the medial temporal lobe regions. Concurrently, cognitive scores derived from instruments like the Mini-Mental State Examination (MMSE), the Alzheimer’s Disease Assessment Scale-Cognitive Subscale (ADAS-Cog), and the Clinical Dementia Rating (CDR) offer quantitative measures of cognitive decline. The increasing availability of large-scale, multimodal medical datasets has enabled the application of machine learning (ML) algorithms to automate and enhance Alzheimer’s detection processes. ML techniques have demonstrated promise in extracting patterns from complex datasets, combining imaging biomarkers and cognitive features, and classifying disease stages with high accuracy. However, developing an interpretable and clinically reliable ML framework that leverages both cognitive assessments and MRI-based image features remains an ongoing research challenge.