Analysis of Machine Learning Modelsto Automatethe Early Detection of AlzheimerDisease

Sardar Un Nisa ()
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Sardar Un Nisa: National University of Modern Languages, Rawalpindi, Pakistan

International Journal of Innovations in Science & Technology, 2025, vol. 7, issue 1, 322-335

Abstract: Alzheimer's disease is anadvancedneurological illness thatprimarilyaffects those over 65. It is characterized by memory loss and cognitive deterioration. Although there isn't a known cure, early intervention can greatly delay the disease's progression, which emphasizes how crucial a prompt and precise diagnosis is. Early-stage identification is still a difficult and time-consuming procedure, though. This study uses machine learning (ML) to improve and speed up Alzheimer's disease detection. The National Alzheimer's Coordinating Center (NACC) dataset, which consists of clinical and genomic data, was subjected to three ML algorithms: Elastic NetClassifier (ENC), Random Forest (RF), and Artificial Neural Network (ANN). Unlike established methodologies that largely rely on Magnetic Resonance Imaging (MRI) paired with other modalities, this research highlights the utilization of limited datasets and comparatively underexplored clinical-genomic data.The models were trained and assessed using the Scikit-learn and TensorFlow frameworks. With an accuracy, F1 score, and recall of 92%, ANN outperformed the other models, indicating its potential for early Alzheimer's identification. This study demonstrates the feasibility of addressing difficulties in early-stage Alzheimer's diagnosis by combining clinical and genomic data with machine learning algorithms.

Keywords: Alzheimer’sDisease; Machine Learning; Classification Algorithms; Artificial Intelligence in Healthcare (search for similar items in EconPapers)
Date: 2025
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