Analysis of Machine Learning Modelsto Automatethe Early Detection of AlzheimerDisease
Journal Title: International Journal of Innovations in Science and Technology - Year 2025, Vol 7, Issue 1
Abstract
Alzheimer's disease is an advanced neurological illness that primarily affects 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 Net Classifier (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.
Authors and Affiliations
Sardar Un Nisa, Azaz Ahmed Kiani, Maria Hilal, Saim Amjid, Moeed Ahmed, Shahzaib Ishtiaq
Keywords
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