Imputing missing values in unevenly spaced clinical time series data to build an effective temporal classification framework

CONCLUSION: The performance of the imputed results proves the effectiveness of TRiBS. Experimental evaluation with the classifiers such as neural networks, support vector machine (SVM) and decision tree have shown an improvement in the classification accuracy when a missing data is pre-processed with the proposed framework.