Designing a Data Mining System to Predict Treatment-Requiring Retinopathy of Prematurity in Neonates: A Pilot Study

Abstract

Background: Nowadays with advanced improvement in NICUs, more preterm infants are surviving with more risks related to ROP. Objectives: The aim of the present study was to collect ROP risk factors and design data mining techniques to suggest a predictive ROP treatment-requiring model. Methods: A cross-sectional study was carried out in an Iranian hospital (2014 - 2018). The population study consisted of 76 preterm neonates with ROP diagnosis. Of all, retinopathy was treated in 35 cases and others had not received any treatment associated with retinopathy. The pre-set questionnaire was used to extract the risk factors leading to treatment-requiring retinopathy. Then specific software models were designed for predicting ROP treatment-requiring model. In order to compare the performance of data mining methods, several performance metrics such as accuracy, precision, sensitivity, specificity, and F-measure have been used. Results: Seventy neonates with ROP entered the study. Results have shown that among four models, Naive Bayes had the best performance with the highest accuracy (87.14), precision (96.43), sensitivity (77.14) and F-measure (85.71). Confusion matrix for Naive Bayes classifier showed that positive predictive value and negative predictive value were 0.7714 and 0.9714, respectively. Overall 87.14% of all data were correctly classified. Moreover, of all data mining techniques, decision tree model could indicate understandable findings as follow; if oxygen therapy continues more than 16 days or blood infusion is > 6 units of packed cells then patients need treatment. Conclusions: The results of the present study have demonstrated that data mining techniques could be effectively implemented in ROP screening programs.