Frequent Pattern Mining of Risk Factors Predicting Neonatal Seizures Outcomes

Abstract

This study aims to present a possible approach to identify the most common combinations of possible risk factors for the outcomes following neonatal seizures. First, we extract important predictor variables from the published studies featuring aspects regarding neurological outcomes in the context of neonatal seizure. Then, we used association rules to build prediction models to determine associations of risk factors which are frequently identified in real-data / evidence based researches. A total of 15 studies and 14 variables were included to identify frequent patterns and generating association rules processe. We searched for accurate and valuable interrelationships between various risk factors. The FP-Growth algorithm generated different itemsets with the largest including four parameters: electroencephalography (EEG), seizures semiology (SO), aetiology (ET), birthweight (BW), with support 0.200. The insights regarding our results may help in creating evidence-based prevention programmes enhancing existing algorithms for diagnosis and treatment of neonatal seizure outcomes.