Multi Variant Deep Transfer Learning Analytics Model for Improved Health Care Solution Using Big Data

The recent surge in healthcare big data has presented
new possibilities of intelligent disease prediction, but currently used analytics models fail to handle heterogeneous data, feature imbalance, and are not generalized across clinical domains. This paper tries to resolve these issues with a new Multi Variant Deep Transfer Analytics Model (DVDTAM) of better healthcare decision support. The aim is to improve the accuracy of disease prediction and reduce the rates of false classification. DVDTAM has a Deep Variant Feature Normalizer that uses the Feature Covariance Value to stabilize heterogeneous distributions of features after which a Multi Variant Deep Transformer with transfer learning is used to model complex clinical dependencies.
Final disease classification is done using a Multi Factor Disease Absorption Weight mechanism. As it is experimentally assessed on the MIMIC-III dataset, DVDTAM reaches a high accuracy of 96.8% and is better than state-of-the-art models by up to 2.5 percent, and the false rate is decreased to 0.05. These findings support the usefulness of the suggested structure of scalable and dependable healthcare analytics.