Accelerating Alzheimer's Research with Machine Learning Models for Improved Detection

The tenacious and fatal neurological disorder Alzheimer's disease poses a growing challenge to modern healthcare. This condition that worsens with time and is characterized by memory loss, cognitive decline, and a loss of independence has a significant effect on both people and society. The relevance of Alzheimer's disease is becoming more and more clear in both the medical and social settings as the world's population continues to age. Alzheimer's disease, which is characterized by the buildup of aberrant protein aggregates in the brain, causes a series of deteriorating alterations that impair cognitive performance. This study investigates the crucial area of Alzheimer's disease prediction, highlighting the value of early detection, better patient treatment, and the revolutionary use of machine learning algorithms. Our goal in this research project is to investigate the use of several machine learning models for the early identification of Alzheimer's disease. An Alzheimer's disease dataset obtained from the Kaggle repository is used, along with four different ML models: Random Forest, Decision Tree, K-NN, and XG Boost. For the purpose of building and assessing these models, this dataset is divided into training and testing subsets. The results obtained from the ML models are then evaluated using a variety of validity scores, such as accuracy, recall, precision, and F1-Score, each of which has a specific function in computing model performance. From the results it is clear, the models from Random Forest and XG Boost stand out for consistently good performance across measures, giving them excellent options for Alzheimer's disease prediction.