NOVEL ENSEMBLE MACHINE LEARNING MODEL DESIGNING FOR ALZHEIMER’S DISEASE DETECTION

Alzheimer's Disease (AD) is the extensive as well as untreatable neurodegenerative disease, as well as the important phase - Mild Cognitive Impairment (MCI) - is still a challenge to diagnose. This is called as a degenerative disease as well as it gets worse over time. AD is primarily a neurodegenerative disorder that is completely incurable. It not only harm mankind memory it also impacts responses to nature, movement and outer stimuli. In addition, Alzheimer's disease disruption the attachment of neurons and damages brain cells. This paper presents a Novel Ensemble Machine learning model designing for Alzheimer’s disease observation. The goal to attain an automatic patient’s classification from the Electro Encephalo Graphy (EEG) biomedical signals of Alzheimer's disease and Mild Cognitive Impairment to assist medical doctor in Identification analysis. Several steps are included in this detection method. Comparative analysis of individual machine learning classifiers as K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree (DT), RF (Random Forest) with Ensemble method (SVM & KNN) is provided in the result section. Obtained Accuracy of ensemble machine learning is 97.5% and similarly, Precision, Recall, F1-Score values are 96.4%, 96.6%, and 97.3% respectively.