Abstract
Periventricular leukomalacia (PVL) is brain injury that develops commonly in neonates after cardiac surgery. Earlier identification of patients who are at higher risk for PVL may improve clinicians' ability to optimize care for these challenging patients. The aim of this study was to apply machine learning algorithms and wavelet analysis to vital sign and laboratory data obtained from neonates immediately after cardiac surgery to predict PVL occurrence. We analyzed physiological data of patients with and without hypoplastic left heart syndrome (HLHS) during the first 12 h after cardiac surgery. Wavelet transform was applied to extract time-frequency information from the data. We ranked the extracted features to select the most discriminative features, and the support vector machine with radial basis function as a kernel was selected as the classifier. The classifier was optimized via three methods: (1) mutual information, (2) modified mutual information considering the reliability of features, and (3) modified mutual information with reliability index and maximizing set's mutual information. We assessed the accuracy of the classifier at each time point. A total of 71 neonates met the study criteria. The rates of PVL occurrence were 33% for all patients, with 41% in the HLHS group and 25% in the non-HLHS group. The F-score results for HLHS patients and non-HLHS patients were 0.88 and 1.00, respectively. Using maximizing set's mutual information improved the classifier performance in the all patient groups from 0.69 to 0.81. The novel application of a modified mutual information ranking system with the reliability index in a PVL prediction model provided highly accurate identification. This tool is a promising step for improving the care of neonates who are at higher risk for developing PVL following cardiac surgery.