Identifying Asthma Risk Factors and Developing Predictive Models for Early Intervention Using Machine Learning

The chronic respiratory illness called asthma causes substantial life quality deterioration for countless people across the world. Adequate diagnosis in the early stages of the condition proves essential for effective treatment which benefits the health status of patients while boosting their productivity levels. Asthma diagnosis shows difficulties in practice because of its clinical similarities with other related respiratory conditions. A research project applies machine learning models to environmental and physiological along with lifestyle data with the purpose of improving asthma diagnosis and forecasting capabilities. A combination of age, gender, familial asthma background, BMI, FEV1/FVC ratio, allergen exposure, AQI, smoking exposure, physical activity levels and diet quality indices serves as independent variables throughout the research assessment. The research depends on data mining methods together with machine learning algorithms including Random Forest, Logistic Regression, and XGBoost to reach exact prediction results. The evaluation metrics consist of accuracy and F1-score together with precision and recall as well as ROC curves to assess model performance. The prediction accuracy reaches 99% for Random Forest and XGBoost while their ROC score reaches 98% which demonstrates their competence in asthma classification. The lower performance of Logistic Regression produced an accuracy of 85% along with an ROC score of 94%. The research results demonstrate that machine learning holds remarkable prospects to transform medical practice when applied to asthma diagnosis and treatment. The use of multiple predictive variables through this diagnostic method leads to much improved diagnostic precision which supports appropriate medical care at the proper time. Future research efforts will concentrate on enlarging the available dataset as well as developing advanced transfer learning methods to optimize the model's functionality for low-resource medical environments. The findings from this research create pathways to develop better diagnostic instruments that enhance asthma treatment approaches for improved patient healthcare.