The primary reason behind dementia, which is distinguished by a decrease in
cognition, freedom within everyday tasks, is Alzheimer's Disease (AD), a disorder that
causes brain cells to deteriorate. AD is addressed as a complex illness, with the
cholinergic and amyloid hypotheses proposed as two primary theories underlying its
pathology.. Additionally, a number of risk factors, such as aging, genetics, head
trauma, vascular disease, pollution, as well as environmental elements, all participate in
the illness. In this chapter, we have presented machine learning-based automatic
identification of Alzheimer’s Disease (AD). As features, Socioeconomic Status, MiniMental State Examination, Estimated total intracranial volume, Normalized wholebrain volume, and the Clinical Dementia Rating have been used. For classification, the
KNN classifier has been used with different distance metrics and by varying the
number of K-values. Here are mainly three types of distance metrics: Euclidean
distance, Manhattan distance, and Minkowski distance, among others, to compute the
performance measures of Accuracy, Recall, Precision, and F1-values.
Keywords: Alzheimer's disease, Accuracy, F1-values, Euclidean distance, KNN classifier, Machine learning, Manhattan distance, minkowski distance, Precision, Recall.
