Machine Learning in Healthcare: Decision Trees for Asthma Risk Prediction

A chronic respiratory condition marked by hyperreactivity and inflammation of the airways., asthma presents serious health problems worldwide. Asthma prediction done early and precisely can result in better patient outcomes and care. The effectiveness of many machine learning algorithms in asthma prediction is investigated in this work., with an emphasis on a performance comparison of Decision Tree., K-Nearest Neighbours (KNN)., and Random Forest classifiers. Created prediction algorithms to detect people at risk of asthma using a large dataset including clinical., environmental., and genetic variables. Among KNN and Random Forest classifiers., the Decision Tree method outperforms them with the maximum prediction accuracy of 81 0/0. The Decision Tree model outperforms others because of its interpretability., which offers precise understanding of the decision-making process., and its capacity to manage complicated relationships between elements. The possibilities of Decision Tree models in asthma prediction are demonstrated by these results., which also emphasize the need of choosing suitable machine learning methods for efficient illness prediction. This work offers a potential method for early asthma diagnosis and customized therapeutic techniques., therefore supporting the continuous attempts to use machine learning in healthcare.