Early Detection of Alzheimer’s Disease with Blood Plasma Proteins Using Support Vector Machines

Abstract: Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that affects memory, cognitive abilities, and daily functioning. Early identification of the disease is important for timely clinical assessment and patient management. This paper presents a machine-learning-based framework for classifying Alzheimer’s disease using biomarker-related data and supervised learning algorithms, with particular emphasis on Support Vector Machine (SVM) and Logistic Regression (LR). The proposed workflow consists of dataset acquisition, data preprocessing, handling missing values, encoding categorical attributes, splitting the dataset into training and testing subsets, model training, and performance evaluation. The models are evaluated using standard classification metrics, including accuracy, precision, recall, sensitivity, and confusion matrix analysis. A web-based interface can make the trained model accessible to users by providing a structured input and prediction workflow. The framework aims to investigate the usefulness of machine learning in supporting the classification of Alzheimer’s disease and to compare the performance of the selected algorithms. However, the reliability of the results depends on the quality, representativeness, and correct identification of the dataset. The proposed system is intended as a research prototype for decision support and not as a replacement for professional clinical diagnosis.

Keywords: Alzheimer’s disease, Blood plasma proteins, Biomarkers, Machine learning, Support Vector Machine, Logistic Regression, Early detection, Classification, Predictive analytics, Healthcare decision support.