Publication

Abstract : Alzheimer’s disease (AD) is among the neurological diseases (dementia) that afflict the elderly most frequently. We introduce a novel machine learning-based approach in this research to differentiate individuals with the early AD classification. Preprocessing, feature selection, training data, and classifiers all affect the outcomes of machine learning-based methods for classifying AD. A novel composite comprehensive MRI development of Alzheimer’s disease is provided in this chapter (AD-DCP-MRI). The results were analyzed in terms of accuracy, precision, recall, and F1-score using the data package that included T1-weighted MRI clinical OASIS temporal data. Our recommendation model is effective for AD categorization, as evidenced by its increased accuracy. These methods can also be successfully applied in the medical field to help with the early identification and diagnosis of disease.