AI-Enhanced Asthma Management: Random Forests Leading the Way

Asthma is a chronic respiratory disease characterized by shortness of breath, wheezing, and the potential for severe, life-threatening attacks. Effective management of asthma is crucial, as inadequate control can lead to serious exacerbations. A reliable system for predicting the likelihood of asthma or asthma attacks can significantly improve patient care. This work employs the Random Forest algorithm to analyse patient health records for asthma prediction. Random Forest, a robust machine learning technique, utilizes an ensemble of decision trees to enhance the accuracy and reliability of predictions. This method is particularly effective in managing complex data and classification tasks. The proposed model achieved an accuracy of 75% in predicting asthma risk. The results demonstrate that Random Forest is a viable method for constructing predictive models in healthcare, with the model assessing factors such as age, medical history, environmental conditions, and genetic predispositions to identify individuals at risk of developing asthma or experiencing asthma attacks.