Abstract :

Alzheimer S Disease Is A Progressive Neurodegenerative Disorder That Affects Memory, Cognitive Function, And Behavior, Posing Significant Challenges To Healthcare Systems Worldwide. Early Prediction And Diagnosis Are Crucial For Effective Management And Slowing Disease Progression. This Study Proposes A Machine Learning-based Approach For Predicting Alzheimer S Disease Using Clinical And Cognitive Data. The System Utilizes Various Supervised Learning Algorithms Such As Decision Trees, Support Vector Machines, And Random Forest To Analyze Patient Data And Identify Patterns Associated With The Disease. Data Preprocessing Techniques, Including Normalization And Feature Selection, Are Applied To Improve Model Performance And Accuracy. The Model Is Trained And Tested On Standard Datasets To Ensure Reliability And Generalization. Experimental Results Demonstrate That The Proposed System Achieves High Prediction Accuracy And Can Effectively Assist Medical Professionals In Early Diagnosis. The Integration Of Machine Learning Techniques Provides A Costeffective, Efficient, And Scalable Solution For Alzheimer S Disease Prediction, Enabling Timely Intervention And Improved Patient Care.

Published:

07-4-2026

Issue:

Vol. 26 No. 4 (2026)

Page Nos:

1372-1377

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to Cite