Improvements in Diagnosing Kawasaki Disease using Machine Learning Algorithms

This research explores the changing landscape of Kawasaki Disease (KD) diagnosis, specifically examining the Machine Learning (ML) algorithms to enhance diagnostic capabilities. KD, a complex pediatric vasculitis, presents diagnostic challenges due to its diverse clinical manifestations, often resulting in delayed treatment. The study investigates the incorporation of machine learning techniques to improve the accuracy and efficiency of KD diagnosis. Utilizing a comprehensive dataset encompassing various clinical parameters related to KD, this research aims to create a robust predictive model. The analysis involves exploring patterns and correlations within the dataset to identify key diagnostic markers for KD. The findings demonstrate the potential of machine learning algorithms to effectively distinguish KD cases from other febrile illnesses, assisting clinicians in making more informed and prompt decisions. The integration of these algorithms in the diagnostic process not only enhances accuracy but also shows promise in reducing diagnostic delays, ultimately contributing to improved patient outcomes.