Paper Title
Alzheimer’s Disease Detection Using Ensemble Machine Learning Approach

Abstract
The research focuses on detecting Alzheimer's disease(AD) at an early stage and explores the use of machine learning to predict the disease using cognitive testing. By combining different machine learning models, the study proposes a novel approach to improve the accuracy of predicting early-stage Alzheimer's disease. This approach involves using Neighborhood Component Analysis and Correlation-based Filtration to identify the most important brain features and a flexible weight matrix process to determine the best models for voting. The study's data suggests that this ensemble method significantly improves the accuracy of early Alzheimer's disease detection.Adding flexible vote improves the accuracy even more, making the results better than those possible with the traditional artificial neural network method. In conclusion, the paper stresses how important it is to find AD early and shows how ensemble machine learning can be used. It ends with the creation of a new method that uses feature selection and adaptive vote to improve the accuracy of AD diagnosis. This method shows how machine learning could change the way early AD is found, making it more accurate than traditional methods and helping us find and treat this terrible disease. Keywords - Adaboost, Artificial Neural networks, Decision Tree, KNN, LR, Random Forest, Support Vector Machine