A Federated MRI and ML Approach for Precision Healthcare Detection

In the context of neurodegenerative illnesses, including Alzheimer's disease (neuroAD), this study employs simulated Federated Learning (FL) techniques to explore decentralized model training and the application of Machine Learning (ML) algorithms, specifically Random Forest and XGBoost, for neu-roAD detection. The research demonstrates promising results, with Random Forest achieving an average recall and accuracy of 94.19%, and XGBoost outperforming with an average recall and accuracy of 95.53% within a FL framework. These findings highlight the potential of ML in early AD diagnosis. Additionally, this study contributes to the broader field of research on the application of ML in healthcare and provides valuable insights into AD and the identification of other diseases. A limitation faced in this research is the use of a desktop computer with high capacity resources since laptop resources are not enough. The study utilizes a public dataset from Kaggle's “Best Alzheimer's MRI Dataset” to support its findings.