Pre-Processing for Early Alzheimer's Detection Using Data Mining


  • R. Malarvizhi, R. Rangaraj


Data Mining, Healthcare, Medical Data, Biomarkers, Dementia, Pre-processing, Disease, Classification.


This paper explores the application of the Synthetic Minority Over-sampling Technique combined with Boosting (SMOTEBoost) for pre-processing in early Alzheimer's detection models. The aim is to address class imbalance by generating synthetic instances and boosting the learning process. Using SMOTEBoost in pre-processing improves machine learning algorithms' learning capacities and helps identify early-stage Alzheimer's patterns more accurately. The effectiveness of the suggested strategy is demonstrated by the experimental results, which also highlight how revolutionary early detection techniques could become. The research presented here advances the likelihood of prompt intervention and better patient outcomes by contributing to the continuous efforts to increase the sensitivity and precision of Alzheimer's diagnosis.


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How to Cite

R. Malarvizhi. (2024). Pre-Processing for Early Alzheimer’s Detection Using Data Mining. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 3691 –. Retrieved from



Research Article