Prediction of Asthma Disease Using Machine Learning Algorithm

Millions of people worldwide suffer from asthma disease, and frequently, early diagnosis and efficient treatment are needed to enhance patient outcomes. Through an analysis of clinical and environmental characteristics, this study investigates a machine learning algorithm for predicting asthma using decision trees, K-Nearest Neighbors, random forests, and the naïve Bayes method. A dataset related to asthma disease is divided into two parts, with the first part for training consisting of around 70% and the second part for testing comprising 30%. Before dividing the subset, SMOTE is applied to balance the dataset because the dataset is unbalanced. Regarding the four algorithms, the decision tree attained better accuracy than the other algorithms. K-NN (K Nearest Neighbor) attained 97.50% accuracy, random forest attained 97.35% accuracy, naïve Bayes attained 69.99% accuracy, and the decision tree attained 67.65% accuracy. In all algorithms, the decision tree performed with high accuracy, as its prediction is 97.65% correct in detection. These algorithms can be applied to related predictive healthcare tasks.