Multi-Modal Data Fusion using Transfer Learning in Big Data Analytics for Healthcare

Through the use of learning transference in the framework of the analysis of large amounts of data, the present study tackles the urgent need for thorough integration of various health care information modalities. We use a descriptive study design incorporating secondary data gathering from credible healthcare sources, drawing upon an interpretivist mindset and an approach that is deductive. Medical imaging, electronic medical histories, genetic characteristics, and data from wearable sensors are just a few of the diverse data sources that are being unified by cutting-edge data preparation and integration approaches. The integrated database serves as the foundation for transfer learning, which optimizes models that were previously trained to derive insightful information. The findings show a significant improvement in the model's ability to identify complex correlations between various data kinds. The approach's precision, specificity, sensitivity, as well as AUC-ROC parameters demonstrate its efficacy. The durability and generalizability of the model across different populations of patients are further established by cross-validation methods. The contrast against benchmark approaches further emphasizes the suggested methodology's advantages in multipurpose data fusion. This work has significant clinical implications in addition to advancing healthcare data analytics. The field of medicine may undergo a revolution as a result of the enhanced diagnostic precision and individualized treatment approaches that result from the multipurpose integration of data and transferable learning, which will eventually improve the health of patients. To ensure seamless integration into standard medical environments, security of information and privacy must be taken into account.