1.3.4 Mortality Prediction
After data pre-processing, normalization, feature extraction and feature reduction, different models are employed to predict the patient’s mortality in an in-hospital stage and calculate the accuracy. The models predict either patient will survive or die. This is determined by using classification technique as mortality prediction is a binary classification problem. This process is done step by step as shown in Figure 1.1.
Table 1.2 Time series variables with physical units [30].
| S. no. | Variables | Physical units |
|---|---|---|
| 1. | Temperature | Celsius |
| 2. | Heart Rate | bpm |
| 3. | Urine Output | mL |
| 4. | pH | [0–14] |
| 5. | Respiration Rate | bpm |
| 6. | GCS (Glassgow Coma Index) | [3–15] |
| 7. | FiO2 (Fractional Inspired Oxygen) | [0–1] |
| 8. | PaCo2 (Partial Pressure Carbon dioxide) | mmHg |
| 9. | MAP (Invasive Mean arterial blood pressure) | mmHg |
| 10. | SysABP (Invasive Systolic arterial blood pressure) | mmHg |
| 11. | DiasABP (Invasive Diastolic arterial blood pressure) | mmHg |
| 12. | NIMAP (Non-invasive mean arterial blood pressure) | mmHg |
| 13. | NIDiasABP (Non-invasive diastolic arterial blood pressure) | mmHg |
| 14. | Mechanical ventilation respiration | [yes/no] |
| 15. | NISysABP (Non-invasive systolic arterial blood pressure) | mmHg |
1.3.5 Model Description and Development
Different models are developed in this chapter to estimate the performance of mortality prediction and comparison between them is also made. The models such as FLANN, Discriminant analysis, Decision Tree, KNN, Naive Bayesian and Support Vector Machine are applied to develop different classifiers. Out of 4,000 records of dataset A 3,000 records are taken as training set and remaining 1,000 records are used for validation or test of the models.
First of all Factor Analysis (FA) is applied to the selected variables to reduce the features. Factor analysis is one of the feature reduction techniques which is used to reduce the high dimension features to low dimension [31]. The 58 features of the dataset are reduced to 49 using FA. Several steps to of factor analysis are
Figure 1.1 Step by step process for mortality prediction.
1 First normalize the data
