Improved Decision Support System for Alzheimer's Diagnosis Using a Hybrid Machine Learning Approach with Structural MRI Brain Scans

Alzheimer's disease (AD) causes damage to brain cells and their activities. This disease is typically caused by ageing, making people over the age of 65 more susceptible. As the disease progresses, it slowly destroys brain cells, making it harder to think clearly, recall things, and do everyday tasks. The end result of this is dementia. Metabolic disorders, such as diabetes and Alzheimer's disease, affect a substantial proportion of the world's population. While there is no permanent cure for AD, early diagnosis can help reduce damage to brain cells and support a faster recovery. Recent research has explored various machine learning approaches for early disease detection. However, traditional ML (Machine Learning) methods and deep learning techniques such as CNNs have not been individually effective in accurately detecting Alzheimer's disease (AD). In this proposed work, we developed a hybrid model that processes sMRI brain images to detect them as demented or non-demented. The model consists of two parts: the first part involves extracting significant features through a sequence of convolution and pooling operations; the second part uses these features to train SVM for binary classification. Data augmentation techniques such as horizontal flipping are used to balance dataset. We calculated key performance metrics essential for the healthcare domain, including sensitivity, specificity, accuracy, and F1-score. Notably, our model achieved an impressive accuracy of 99.60% in detecting AD, with a sensitivity of 99.83%, a specificity of 99.40%, and an F1-score of 99.58%. These results were validated using 15-fold cross-validation, enhancing the model's robustness for new data. This approach yields a more robust model, offering greater accuracy and precision compared to existing methods. This model can effectively support manual systems for detecting AD with greater accuracy.