Early Diagnosis of Alzheimer’s Disease Using Machine Learning Methods
Year: 2025
Type: conference-paper
Abstract: This study comprehensively examines the role of machine learning techniques in detecting the early stages of Alzheimer’s disease (AD). Significant advancements in medical technologies have provided higher quality and more comprehensive datasets for early disease detection, which is critical for managing chronic and irreversible conditions like Alzheimer’s. Alzheimer’s disease is a neurological condition that results in cognitive decline, brain atrophy, and ultimately death, and is a primary cause of dementia in older adults. Early diagnosis is vital for slowing the progression of the disease and enabling more effective treatment options. In this context, our study aims to detect the early stages of Alzheimer’s disease through the application of machine learning methods. The study utilizes the Open Access Series of Imaging Studies (OASIS) data to make predictions. The effectiveness of the machine learning models is assessed through metrics like accuracy, precision, recall, and F1-score, and the results are analyzed within a structured framework. Results indicate a notably successful performance, achieving an average accuracy of 85% on test data for AD. This accuracy score demonstrates a significant advantage compared to other studies in the existing literature. These findings highlight the potential of machine learning applications as effective tools in the early diagnosis of neurological diseases. Additionally, the results underscore the importance of data-driven approaches and modern technologies in managing Alzheimer’s disease, offering new opportunities for enhancing healthcare quality. This study aims to illuminate the potential of machine learning techniques and contribute to the development of innovative solutions for the early diagnosis of Alzheimer’s and similar diseases in the future.
Source: Procedia Computer Science
Authors: Seda Demir, Harun Selvitopi
Institution: Erzurum Technical University
Language: English
Subfield: Psychiatry and Mental health
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4410253271