Childhood Asthma Disease Prediction Using Classification Algorithms of Supervised Machine Learning

Asthma is a very dangerous lung disease in which our airways are swollen and become narrow and blocked. Air does not flow through narrow airways properly. It is an ongoing disease and does not go away, regular treatment is needed lifelong. According to the survey report, 25 million people in the United States are living with this disease and out of that 5 million are children who are asthma patients. Asthma is a life-threatening disease if don’t get treatment timely, can lose your life. Early prediction of this disease among children not only provides a recovery solution for the disease but also helps to increase their lifetime. The proposed designed Intelligent system provides useful resources to the healthcare system to make accurate decisions for preventing asthma in childhood. The research paper aims to design an intelligent artificial-based machine learning classification model to predict asthma disease among children aged less than 10 years. Research also included a comparative analysis of different classification methods of machine learning like Linear regression, KNN Model, Decision Trees, Random Forests and Support Vector machines. It also helps to select the best model by analyzing the performances of these algorithms based on efficiency and accuracy. The outcomes of the research paper to study asthma prediction using different classification models and help doctors and patients to take the right decision timely.