MACHINE LEARNING APPROACH FOR EARLY ALZHEIMER’S DETECTION USING COGNITIVE FEATURES

Abstract

Early detection of Alzheimer’s Disease (AD) is critical for slowing disease progression and improving patient quality of life. This study proposes an intelligent framework titled “Early Detection of Alzheimer’s Disease Using Cognitive Features: A Voting-Based Ensemble Machine Learning Approach”, which leverages advanced Machine Learning (ML) techniques to identify early signs of cognitive decline. The system focuses on analyzing cognitive features such as memory performance, language ability, attention span, problem-solving skills, and behavioral patterns collected through clinical assessments and standardized tests. The proposed methodology utilizes a Voting-Based Ensemble Learning Model, which combines the predictive strengths of multiple base classifiers such as Random Forest, Support Vector Machine (SVM), Logistic Regression, and KNearest Neighbors (KNN). By aggregating predictions using majority voting or weighted voting strategies, the ensemble model improves classification accuracy and robustness compared to individual models. Data preprocessing techniques including normalization, feature selection, and handling missing values are applied to enhance model performance. Additionally, the system incorporates feature importance analysis to identify the most influential cognitive indicators associated with early-stage Alzheimer’s. Experimental results demonstrate that the proposed ensemble approach achieves higher accuracy, precision, recall, and F1-score compared to traditional single-model approaches. The system effectively distinguishes between healthy individuals, mild cognitive impairment (MCI), and early Alzheimer’s patients. This approach not only enhances diagnostic reliability but also supports clinicians in making informed decisions. In conclusion, the integration of cognitive feature analysis with ensemble machine learning provides a powerful and scalable solution for early Alzheimer’s detection. The proposed system has the potential to be deployed in clinical decision support systems, enabling timely intervention and improved patient care