Review paper of Alzheimer's Disease Detection for Imbalance Dataset using machine Learning
Keywords:
Deep Learning; Alzheimer’s Disease; MRI; Early Diagnose.Abstract
Alzheimer's Disease is a progressive neurodegenerative disorder that affects memory, thinking ability, and cognitive functions, primarily occurring in elderly individuals. Early detection of Alzheimer’s disease is essential for effective treatment planning, patient monitoring, and slowing disease progression. Traditional diagnostic methods rely on clinical examinations, cognitive assessments, and brain imaging techniques, which are often time-consuming, costly, and dependent on expert analysis. In recent years, machine learning approaches have shown significant potential in the automated detection and classification of Alzheimer’s disease using medical datasets and neuroimaging data. However, one of the major challenges in Alzheimer’s disease prediction is the imbalance present in medical datasets, where the number of healthy samples is significantly higher than diseased cases, leading to biased model performance and poor minority-class prediction accuracy.
This review paper presents a comprehensive analysis of machine learning techniques used for Alzheimer’s disease detection on imbalance datasets. The study also discusses different imbalance handling techniques including Synthetic Minority Oversampling Technique (SMOTE), undersampling, oversampling, cost-sensitive learning, and hybrid sampling approaches. In addition, preprocessing methods, feature extraction techniques, neuroimaging datasets, and evaluation metrics such as accuracy, precision, recall, sensitivity, specificity, and F1-score are analyzed. Comparative analysis indicates that machine learning models combined with imbalance handling techniques significantly improve Alzheimer’s disease prediction performance and reduce classification bias. The review concludes that intelligent machine learning systems provide reliable and efficient solutions for early Alzheimer’s disease diagnosis and can support healthcare professionals in clinical decision-making and patient care management.
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