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Predicting mortality of ICU patients using statistics of physiological variables and support vector machines

We began using the same variables as SAPS-1 score, adding the rest of variables one by one as recommended by physicians, to observe whether the SVM classification improves. These variables include: Age, HR, SysABP, NISysABP, Temp, RespRate, MechVent, Urine, BUN, HCT, WBC, Glucose, K, Na, HCO3, GCS, and other variables that were added for phase 1: DiasABP, NIDiasABP, Cholesterol, Creatinine, and SaO2. We found a 6.1% error in the Set-A files due to the absence of measures such as: RespRate, Temp, and age. To solve for these errors on phase 1 we chose to input values within the normal range for these physiological variables. We calculated: mean, standard deviation, and range of variation (max and min) for each one of the physiological variables. These values were placed in nodes corresponding to an index and a value of the variable, which were escalated between 0 and 1. We created a matrix where the columns corresponded to: means and standard deviations of the input variables, and rows corresponded to the individual patient's records. We decided to use SVM. Five SVM machines were tested and scored. To conclude, we demonstrate the applicability of SVM for predicting mortality of ICU patients with a final score using set-B of 0.350352 for event 1.