Multimodal fusion architectures for Alzheimer's disease diagnosis: An experimental study.

Journal: Journal of biomedical informatics
Published Date: May 8, 2025

Abstract

OBJECTIVE: In the attempt of early diagnosis of Alzheimer's Disease, varying forms of medical records of multiple modalities are gathered to seize the interaction of multiple factors. However, the heterogeneity of multimodal data brings a challenge. Hence, the role of artificial intelligence comes into play to provide the medical practitioner assistance in making diagnosis and prognosis. In order to be adopted as a clinical decision support system, interpretable or explainable model is important for healthcare professionals to trust the results. This study assessed various popular machine learning models under two multimodal fusion architectures to find the best combination in terms of both predictive performance and interpretability.

Authors

  • Florence Leony
    Department of Industrial Engineering and Management, Yuan Ze University, Taoyuan, 320, Taiwan, ROC; Department of Industrial Engineering, Universitas Kristen Maranatha, Bandung, 40164, Indonesia.
  • Chen-Ju Lin
    Department of Industrial Engineering and Management, Yuan Ze University, Taoyuan, 320, Taiwan, ROC. Electronic address: chenju.lin@saturn.yzu.edu.tw.

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