Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Chatterjee, Shubham; Sharma, Vandana; Mishra, Sushruta; Alkhayyat, Ahmed Title A name given to the resource Comparative Analysis of Predictive Models to Detect Alzheimers Disease Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1409 LNNS;pp.387-396 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-6300-2_27" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-6300-2_27</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105015717200?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105015717200?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant 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 Description An account of the resource 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. Subject The topic of the resource Alzheimers disease; Comparison; Detection; Machine learning; Medical healthcare Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981966299-9; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online