Machine Learning-Based Risk Prediction of Asthma

This paper will attempt to detect asthma early based on machine learning. This work utilizes a dataset consisting of 21 features such as family history of asthma, intake of drugs, smoking status, and quality of air and much more. The work tries to develop models with maximum precision, recall, and accuracy in asthma classification. These include several machine learning algorithms evaluated: SVM, Decision Trees, and Random Forest. This study focuses on both medical and environmental data when used in combination. The results here show that the Random Forest model performed well in achieving an overall high performance, with an accuracy of 0.926829. This has dire implications for evaluation and early diagnosis of Asthma. Key Words: Asthma detection, Machine learning, Patient history, Environmental factors, Support Vector Machines (SVM), Decision Trees, Random Forests, Predictive models, Air quality, Smoking habits, Medication usage, Family asthma history, Ac- curacy, Precision, Recall, Early diagnosis, Healthcare, Medical data, Environmental data, Predictive power