Prediction of cyanotic and acyanotic congenital heart disease using machine learning models.

Background: Congenital heart disease is most commonly seen in neonates and it is a major cause of pediatric illness and childhood morbidity and mortality.

Aim: To identify and build the best predictive model for predicting cyanotic and acyanotic congenital heart disease in children during pregnancy and identify their potential risk factors.

Methods: The data were collected from the Pediatric Cardiology Department at Chaudhry Pervaiz Elahi Institute of Cardiology Multan, Pakistan from December 2017 to October 2019.

Machine learning methods for predicting major types of rheumatic heart diseases in children of Southern Punjab, Pakistan.

Objective: Rheumatic heart disease (RHD) is a major health problem in the world, particularly in developing countries. This study aimed to predict mitral regurgitation (MR) and mitral stenosis (MS) RHD among children with RHD.

Methodology: Data was collected from the Pediatric Cardiology Department at Chaudhry Pervaiz Elahi Institute of Cardiology Multan, Pakistan from March to October 2019.