A Mortality Prediction System for Neonatal Jaundice Using Machine Learning Techniques

Abstract

Abstract Birth complications, especially jaundice, are a leading cause of child death and morbidity across the world. The severity of these diseases may decrease if researchers learn more about their origins and develop effective treatments. Certain advancements have been made, but they are insufficient. Newborns often have jaundice as their primary medical issue. Jaundice may be brought on by a variety of factors. An elevated bilirubin level is a hallmark of jaundice. The incidence of hyperbilirubinemia in newborns is highest during the first postnatal week. The inability to detect problems early enough to get prompt treatment, as well as the similarity of symptoms that may lead to misdiagnosis, are both potential causes of failure. The situation is far worse for Ethiopia and other countries already in distress. A lack of paediatricians and neonatologists might be a reason for alarm. Due to a lack of appropriate diagnostic tools, experts in newborn health are often forced to rely their judgements only on interviews. It's probable the interviewer didn't know much about contagious diseases in infants. This suggests there is room for a faulty or insufficient diagnosis. For machine learning to make accurate forecasts, sufficient amounts of relevant past data must be made available. Jaundice has a high mortality rate, however this may be reduced with prompt identification and classification. The diagnostic accuracy of illnesses may be enhanced by using machine learning techniques. In this essay, I do a deep dive into medical data mining and pull out all the stops to provide you the information you need. It is necessary to investigate, analyse, extract, choose, and categorise the characteristics. Finally, it offers some therapeutic ideas. It helps the doctor diagnose jaundice faster so that effective therapy may be started sooner. The procedure is simplified and made more natural with the use of computer vision and machine learning methods. The refined method of classification improves accuracy. Using a classification stacking method, we found that the top causes of mortality in newborns include serious infections, birth asphyxia, necrotizing enterocolitis, and respiratory distress syndrome. Most infant fatalities may be traced back to these three factors. Dates included in the data set are 2018 through 2021. Support Vector Machine (SVM) performed best when pitted against the newly developed stacking model, XGBoost (XGB), Random Forest (RF), and other machine learning models. The proposed stacking model performed better than its competitors in terms of accuracy (97.04 percent). This is important because we hope it will help hospitals, particularly those with less resources, detect infant diseases sooner.