Federated Explainable AI-Based Alzheimer’s Disease Prediction With Multimodal Data

Alzheimer’s Disease (AD) is a progressive neurological disease that severely impairs cognitive function. Early detection is critical for effective treatment and management. Machine Learning (ML) methods are often used to ensure early detection and prediction. However, ML has various issues, including the data island problem. The fragmentation that results from the data island problem makes building reliable, effective ML models more complex, and it is particularly problematic in industries where privacy is a concern, like healthcare. Federated Learning (FL) can help tackle the data island problem by keeping sensitive patient data decentralized and enabling many institutions to work together on model training without exchanging raw data, all while maintaining privacy compliance. As Random Forest (RF) is proven to be the best-performing classifier in this research, an RF classifier is used to create FL. The model incorporates multiple data modalities, such as Magnetic Resonance Imaging (MRI) segmentation and clinical and psychological data, to capture the variety of characteristics influencing the progress of AD. Another concerning issue with ML is its uninterpretable character. We use SHapley Additive exPlanations (SHAP) Explainable Artificial Intelligence (XAI) techniques that emphasize important factors impacting model decisions in order to improve predictability and transparency. This explainability promotes confidence in AI-based diagnoses by enabling researchers and physicians to comprehend the underlying mechanisms guiding the predictions. The combination of XAI, FL, and Open Access Series of Imaging Studies (OASIS-3) Multimodal data offers an interpretable, scalable, reliable, and privacy-centered solution for multiple complex issues, such as predicting AD. This approach results in better diagnosis precision, greater security, and increased confidence in AI technologies, making it a novel methodology in medical sciences. With data privacy maintained, our method produces 98.93% accurate predictions, providing a solid detection strategy for AD. The suggested approach’s F1-score, Precision, Recall, and AUC are 98.93%, 98.94%, 98.93%, and 99.97%, respectively. This work also shows that a multimodal dataset performs better than a single modal dataset.