Exploratory Investigation and Alzheimer's Disease Classification Utilizing Ensemble Models in Machine Learning

The Alzheimer's disease (AD) is a highly prevalent neurodegenerative disorder that affects an estimated 20 million people worldwide. It is characterized by changes in behavior, deteriorated cognitive abilities, and memory loss. For prompt intervention and efficient treatment of Alzheimer's disease (AD), it is imperative to identify and diagnose the disease as early as possible. In recent years, machine learning methods have become increasingly popular for detecting Alzheimer's disease (AD), with clinical data being used as the main source of information. This paper provides a comprehensive analysis of the detection of Alzheimer's disease using machine learning techniques. In this research, several key components are included, including data preprocessing, visualization of feature relevance, and evaluation of performance for five ensemble classifiers: Random Forest, AdaBoost, Gradient Boosting, Extra Trees, and Bagging. Principal Component Analysis (PCA) is used to reduce the number of attributes of a model and better understand the importance of particular features within the dataset. In addition, the research rigorously evaluates the efficacy of the classifier by utilizing a distinct test dataset. The models' prediction powers are assessed by calculating and reporting various metrics, including accuracy, precision, recall, and Fl-score.