Comparative Analysis of Predictive Models to Detect Alzheimers Disease
subject: Alzheimers disease; Comparison; Detection; Machine learning; Medical healthcare
description: Alzheimers disease is the most common type of dementia, often affecting people above the age of 60, as all the brain connections and cells themselves start to die, affecting motor, speech and memory, slowing eating away a person once it sets out as it is a non-curable disease as of now. But an early and easy diagnosis may help slow down the process and start treatment, so it is essential to diagnose it quickly. But this disease needs a number of tests and time to determine the diagnosis, and time is of the essence. Various Machine Learning (ML) algorithms are being applied nowadays, with newer methods being trialed every day for the detection of Alzheimers more consistently and easily, but it is essential to apply the most accurate of models and require only the optimum number, and cost efficient tests for reliable diagnosis so this horrid disease could be started the treatment for as soon as possible. This paper is presenting its arguments for various methods of prediction of Alzheimers to improve efficiency of detection, a comparison of models taking into consideration the costs, the accuracy and the true benefit of the test for early tackling of this illness. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
creator: Chatterjee, Shubham; Sharma, Vandana; Mishra, Sushruta; Alkhayyat, Ahmed
source: Lecture Notes in Networks and Systems;Volume;1409 LNNS;pp.387-396
publisher: Springer Science and Business Media Deutschland GmbH
date: 01-01-2025
rights: Restricted Access; Hardcopy may be available in the library
relation: ISSN: 23673370; ISBN: 978-981966299-9;
language: English
type: Conference paper
coverage: Chatterjee S., Kalinga Institute of Industrial Technology, Bhubaneswar, India; Sharma V., Computer Science Department, CHRIST University, Bengaluru, India; Mishra S., Kalinga Institute of Industrial Technology, Bhubaneswar, India; Alkhayyat A., College of Technical Engineering, The Islamic University, Najaf, Iraq