Advancing Alzheimer’s Diagnosis Through Machine Learning: A Refined Methodology For Early Detection And Classification

Alzheimer is a type of dementing illness that leads to gradual and progressive degeneration of the patient’s memory as well as other cognitive skills. This can be attributed to lacking of diagnosis and treatment of this disease the incidences of which are expect to double within the couple of coming years. For structural brain changes the technique adopted is Magnetic Resonance Imaging (MRI) and for the cognitive score Mini Mental State Examination chart is used. Moreover, age, gender, and genetic predisposition also hold some clue as to how the manifestations of the disease would be like.This information raises the predictability of the actual state of Alzheimer’s disease using MRI, MMSE, and some basic clinical indexes. A comparison is carried out on k-Nearest Neighbors (kNN), Random Forest, and Support Vector Machine (SVM). SVM is useful when working with high dimensionality, Random Forest is a good application of decision trees, and kNN is a helpful guide in pattern identification. This paper has therefore implemented the use of multidimensional sources in fusing data and the incorporation of hard techniques in machine learning to increase the precision of the diagnosis which consequently enables early treatment of the developers of Alzheimer’s disease as well as improving their quality of living.