Biomedical Data Mining for Information Retrieval

Support Vector Machine [35] is a supervised machine learning algorithms which aims to find a hyperplane in the N-dimensional space. A plane which has the maximum margin is to be chosen. Vectors are information focuses that are nearer to the hyperplane and impact the position and direction of the hyperplane. Utilizing these help vectors, the edge of the classifier is expanded. Erasing the help vectors will change the situation of the hyperplane. These are the focuses that assist in building the SVM.

The results of all the models on testing set containing 1,000 records are shown in the Table 1.3.

As exhibited from the above table DT has outperformed the other five models with an accuracy of 97.95%. FA-FLANN model has secured the 2nd rank with an accuracy of 87.6%. DA, KNN and SVM models are giving almost same results with accuracy of 86.05%, 86.6% and 86.15% respectively. The worst result is reported for the Naïve Bayesian based model with an accuracy of 54.80%.

Table 1.3 Comparison of different models during testing.

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.

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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