Stacked Ensemble Improvement of the Lower Respiratory Tract Infection Diagnoses in Peadiatric

Abstract

Lower Respiratory Tract Infections (LRTIs) are the second and third causes of pediatric patients' death in Nigeria and the United States of America. It is observed from several reviewed literature that the LRTIs accounted for more than a million children morbidity and mortality yearly due to lack of prompt diagnosis or no diagnosis due to a shortage of medical experts and medical facilities in our localities. Intense research is ongoing on applying machine learning (ML) to its clinical diagnosis and reducing its spread in pediatric patients. In this research, K-Nearest Neighbor (KNN), C4.5 Decision Tree, and Naive Bayes' ML algorithms were used to develop three base diagnosis models with Correlation, consistency, and information gain selected feature of the LRTI dataset, Multiple Model Trees (MMT) Meta algorithm is used to combine and improve the diagnoses of all the base models using stacked ensemble. The preliminary diagnosis findings using base models have established that the information gained feature extraction method performed much better than the other two. It, therefore, suffix that the results from this should be used for further processing. All the models built with the reduced feature set recorded improved diagnoses accuracy more than the model built with the whole feature set. The MMT stacked ensemble models recorded an improvement on the diagnosis of LRTIs in Peadiatric, it recorded the highest diagnostic accuracies improvement of 12.80%, 13.52%, and 12.37%, and lowest diagnostic accuracies improvement of 6.37%, 5.22%, and 6.09% with the MMT stacked ensemble models of the Consistency, the Correlation, and the information gain reduced selected feature set respectively. These experimental results show the potential for this approach to deliver a reliable and improved diagnosis of LRTIs. It is recommended to be used to diagnose LRTIs in primary health care centers to reduce its mortality rate.