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
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.
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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