Dynamic Bayesian network and hidden Markov model of predicting IoT data for machine learning model using enhanced recursive feature elimination

The research work develops a Context aware Data Fusion with Ensemblebased Machine Learning Model (CDF-EMLM) for improving the health data treatment. This research work focuses on developing the improved context aware data fusion and efficient feature selection algorithm for improving the classification process for predicting the health care data. Initially, the data from Internet of Things (IoT) devices are gathered and pre-processed to make it clear for the fusion processing. In this work, dual filtering method is introduced for data pre-processing which attempts to label the unlabeled attributes in the data that are gathered, so that data fusion can be done accurately. And then the Dynamic Bayesain Network (DBN) is a good trade-off for tractability becoming a tool for CADF operations. Here the inference problem is handled using the Hidden Markov Model (HMM) in the DBN model. After that the Principal Component Analysis (PCA) is used for feature extraction as well as dimension reduction. The feature selection process is performed by using Enhanced Recursive Feature Elimination (ERFE) method for eliminating the irrelevant data in dataset. Finally, this data are learnt using the Ensemble based Machine Learning Model (EMLM) for data fusion performance checking.