Machine Learning for Predicting Development of Asthma in Children

摘要

Asthma in children needs to be identified as early as possible to provide children with medical intervention. Creating a model that accurately predicts asthma in children has proven difficult. In the current state, research has provided models for asthma prediction that have low accuracy and use a small, specific sample size. There are limited research that analyzes a large population of children, using specific factors, to develop a model that can be used in a clinical setting. In this paper, we developed predictive models to analyze a child asthma health dataset. Machine learning classifiers are used to develop these predictive models; including Linear Regression, Decision Tree, Random Forest, KNN, and Naive Bayes technique. Of all the classifiers implemented, random forest classifier resulted in highest prediction accuracy (90.9%). Following are the variables: Sex, Difficulty Breathing, Allergies, and Medication have the highest correlation with asthma. The review of current research and the results of model that are presented in this paper can be used in a clinical setting by medical professionals to make predictions of asthma development in children and implement early intervention for the treatment of asthma development.