Revolutionizing Asthma Control Using Random Forest Machine Learning Enhancement Technique

Breathlessness, wheezing, and frequently fatal bouts are hallmarks of asthma, an inflammatory and airway-induced lung disease. Asthma sufferers must be able to anticipate major exacerbations brought on by uncontrolled asthma. Thus, a framework is required that can most accurately forecast a patient's chance of developing asthma. In this work, the Random Forest algorithm has been employed to mine the data including patients' past health records in order to provide an efficient mechanism for asthma disease prediction. In machine learning, it is a strong ensemble learning technique extensively applied to classification and regression problems. Its resilience, scalability, and high dimensionality handling of complicated datasets are well known. Being a member of the tree-based method family, Random Forest gets its power from combining the predictions of several decision trees. We have forecasted with 75% accuracy after applying the random forest class. After it was demonstrated that the Random Forest can be used to create an asthma disease prediction model. The model looks for those who are at risk of getting asthma or having flare-ups by examining a number of patient-related variables, including demographics, medical history, environmental exposures, and genetic predispositions.