1.5 Conclusion
In this chapter, different algorithms are presented to predict in hospital mortality based on the information collected at the hospital from the 48 h of observation. The data are selected from the PhysioNet challenge 2012 and used to predict in-hospital death. 4,000 records of patients have been selected of set A, from which 3,000 records of patients are used for training and other 1,000 records are kept for testing. 15 time series variables are selected out of 41 features for model development. Missing values are handled by imputing zeros. Six different models are developed for mortality prediction and a comparison is performed. It is observed from comparison that the decision tree is one of the best algorithms which obtained best accuracy result as compared to other five models used for the simulation study.
1.6 Future Work
Many authors have accepted challenges of PhysioNet challenge 2012 and published many papers and found better accuracy results. Mortality prediction is still a challenging task to predict patient’s mortality in a hospital. Researchers are going on to develop some more models, other methods of handling missing data and make new strategies for mortality prediction. The performance of different other algorithms such as extreme learning machine, convolution neural networks and deep learning can also be used for the purpose in future.
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