## 3 Data Fusion and Multimodal Integration  
Data fusion and multimodal integration are essential components of fault diagnosis systems, particularly when dealing with various data sources such as sensor readings, images, and textual information. These techniques improve diagnostic accuracy by merging information from different modalities and addressing the inherent uncertainty in real-time environments [31; 32]. Figure 1: An illustration of an ensemble learning model that combines several base classifiers (such as decision trees and neural networks) for fault diagnosis. By combining the predictions of multiple models, this approach boosts robustness and accuracy, providing a more reliable overall decision. Multimodal data fusion has gained significant traction across various industries, especially in healthcare, where it enhances predictive performance and provides deeper insights by integrating data from diverse sources. Fusion strategies are typically categorized as early, intermediate, or late fusion each classification depending on the stage at which data integration takes place. Early fusion merges raw inputs, delivering simplicity but may face noise and high dimensionality challenges. In contrast, late fusion compiles outputs from separate models, promoting modularity yet risks neglecting cross-modal relationships. Intermediate fusion acts between the input and decision phases, enabling advanced feature-level integration that generally harmonizes the advantages and drawbacks of the other two techniques. This section delves into these fusion strategies in depth, focusing on their implementation in healthcare applications where integrating multimodal data is essential for delivering accurate diagnoses, effective treatment plans, and comprehensive patient care. * **Early fusion:** This method integrates data from multiple modalities at the input level, feeding them into a single unified model. Inputs may consist of raw signals, handcrafted features, or deep features. Common techniques for early fusion include concatenation, element-wise summation, multiplication (Hadamard product), and bilinear pooling (Kronecker product). One major advantage of early fusion is the simplicity of working with a single model; however, it assumes that the model can effectively handle all modalities. Synchronization is also necessary, which may pose challenges when data are captured at different time points. In healthcare, early fusion is applied in tasks such as combining ultrasound imagery for breast cancer diagnosis or merging imaging data with electronic medical records (EMRs) for applications like skin lesion classification and cervical dysplasia prediction. It is also used to integrate genomics with histology or radiology data for cancer classification, survival prediction, and treatment response analysis. As shown in Figure 2, early fusion integrates multiple inputs at the raw level, allowing a unified model to process them jointly. This approach is often challenged due to data synchronization issues. * **Late fusion:** Also referred to as decision-level fusion, this approach trains separate models for each modality and then combines their predictions. Methods such as averaging, majority voting, or Bayesian inference are commonly used. Since synchronization is not required, different architectures can be applied independently to  
Figure 2: In early fusion, we combine multiple inputs at their raw level, enabling a single model to process them together. However, this approach often faces challenges related to synchronizing the data. each modality. This is especially beneficial in scenarios involving heterogeneous or incomplete data. Furthermore, new modalities can be added without retraining the entire model. Late fusion is well suited to cases where modalities are loosely related. In healthcare, it is used in applications like combining MRI data with PSA blood tests for prostate cancer detection or integrating genomics with histology data for survival prediction.