Comparison of predictive models to predict survival of cardiac surgery patients

With recent innovation in computer database technology, voluminous data related to cardiac surgery are easily stored and made available for further analysis. However, these large quantities of data are often not fully utilized in terms of modelling cardiac surgery outcomes. Most of the previous studies have mainly focused on applying statistical techniques to small sample of data in order to reveal the simple linear relationships between the factors and survival of cardiac patients. Data mining offers a significant advantage over conventional statistical techniques which often requires the normality assumption. This study developed and compared new models to predict the survival of cardiac surgery patients. The dataset consists of 5154 observations with 23 variables, as suggested by domain experts from a renowned heart-surgery centre in Malaysia. After the data cleaning process, a total of 4976 cases and 12 variables were used for further analysis. The three predictive models, namely; Logistic Regression, Decision Tree and Artificial Neural Network, were developed and compared using the classification accuracy rate, sensitivity and specificity. From the Logistic Regression using ENTER selection model, the whole sample with 4976 cases had an imbalanced class case which led to biased results. Therefore, using the undersampling technique suggested by [14], a sample of 1209 cases (17% died and 83% alive) was used and further analysis was performed using this sample. Results showed that Artificial Neural Network is the best predictive model with classification accuracy, sensitivity and specificity of 88.4%, 95.67% and 58.06% respectively.