Optimizing Early Alzheimer's Detection: Evaluating the Performance of SVM, Random Forest, and CNN Models
Keywords:
Alzheimer's disease, Support Vector Machines, Random Forest, Convolutional Neural Networks, predictive modelling.Abstract
Alzheimer's disease (AD) is a leading cause of dementia, presenting significant challenges to healthcare systems worldwide. Early detection is crucial for timely intervention, which can increase patient results and quality of life. This study explores the a pplication of Machine Learning (ML) models to enhance early detection of AD. We utilized a diverse dataset from Kaggle, comprising clinical, demographic, and neuroimaging information. Three ML models—Support Vector Machines (SVM), Random Forests, and Convolutional Neural Networks (CNN)—were trained and evaluated using this dataset. Comprehensive preprocessing steps, including data cleaning, normalization, and feature extraction, were applied to ensure data quality. The performance of the models was evaluated using various metrics, including accuracy, precision, recall, F1-score, and the Area Under the Receiver Operating Characteristic Curve (AUC-ROC). Among the models, the CNN model excelled, attaining 91% accuracy, 89% precision, and 90% recall, F1-score of 90%, and AUC-ROC of 0.95. However, the high computational demands of CNNs and dataset diversity limitations were noted as significant challenges. Forthcoming research may focus on expanding the dataset, optimizing model architectures, and integrating additional biomarkers to improve model generalizability and practical application in clinical settings. This study brings out the potential of ML, particularly CNNs, in the early detection of Alzheimer's disease, flagging the manner for improved diagnostic tools and patient care.
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