International Journal of Computer Techniques Volume 12 Issue 4 | Data-Driven Prediction of Early Alzheimer’s Disease Through ML Algorithms
Data-Driven Prediction of Early Alzheimer’s Disease Through ML Algorithms
Authors:
Ms. A. Kamatchi, Research Scholar
Dr. V. Maniraj, Associate Professor & Research Supervisor
Department of Computer Science, A.V.V.M. Sri Pushpam College (Autonomous), Poondi, Thanjavur (Dt), Affiliated to Bharathidasan University, Tiruchirappalli, Tamil Nadu
Emails: kamatchia06@gmail.com, manirajv61@gmail.com
Journal: International Journal of Computer Techniques (IJCT)
Volume: 12 | Issue: 4 | Publication Date: July – August 2025
ISSN: 2394-2231 | Journal URL: https://ijctjournal.org/
Abstract
This paper presents a machine learning-based approach for early prediction of Alzheimer’s disease using the OASIS dataset. Algorithms including Decision Tree, Random Forest, SVM, Gradient Boosting, and Voting classifiers were evaluated. The proposed model achieved over 90% accuracy, outperforming previous methods. Early diagnosis supported by ML can reduce AD-related mortality and improve treatment outcomes.
Keywords
Alzheimer’s Disease, Machine Learning, Early Diagnosis, Voting Classifier, OASIS Dataset, Chi-Square, Mortality Reduction
Conclusion
The study confirms that ML algorithms can effectively predict early-stage Alzheimer’s disease. The integration of these models into clinical workflows can enhance diagnostic accuracy and reduce mortality. Future work may include deep learning and multimodal data fusion for improved prediction.
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