Performance Enhancement of Predictive Analytics for Health Informatics Using Dimensionality Reduction Techniques and Fusion Frameworks

Abstract

Predictive analytics has become an essential area of research in health informatics. The availability of multi-source and multi-modal data in healthcare has made the disease prediction, diagnosis, and medication process more effective and reliable. However, the analysis and decision making have become challenging task, particularly when data is in multiple formats and from different sources. In this study, different frameworks have been proposed to handle multi-nature data at different levels for predictive analytics. Dimensionality reduction techniques have been applied to extract relevant features to enhance the analysis. To improve the performance of predictive analytics at different fusion levels, the potential benefits of multi-modal data have been discussed. Moreover, notable improvement in prediction accuracy has been observed through experimental evaluation of the proposed frameworks. Furthermore, the issues which have been found during dimension reduction and fusion approaches have also been highlighted.

A Deep Learning Approach for Prediction of Protein Secondary Structure

“…The best parameters values attained using hyperparameter tuning for epochs, batch_size, and optimizer attained are 20, 128, and Adam, respectively. Furthermore, dimensionality reduction techniques were employed [38].…”