A Machine Learning Approach to Explore the Intersection of Biomarkers and Asthma Control

Introduction: Clinical decision support systems backed by machine learning approaches are widely deployed at primary care centers for the prediction of diseases. New findings for the ongoing study in the field of clinical informatics, incorporating biomarker data, have recently focused on studies that validate the conclusions made from the biomarker data. The intersection of biomarkers and asthma control is a rapidly advancing field that holds promise for improving personalized care. Materials and Methods: The work presented in the paper proposes the implementation of a machine learning framework based on regularization techniques for the assessment of various asthma control levels by discovering the distinctive data via an embedded approach. The approach is seen to effectively identify the best of the features that mark the cytokine and cellular profile of subjects to identify the predictive biomarkers. results: The performance evaluation results of the various classifiers using the predominant cytokine combination identified in the reduced set yielded a classification accuracy of 80% when compared to 64% classification accuracy that was obtained with when the entire cytokine profile was used without involving any feature selection technique Results: The performance evaluation results of the various classifiers using the predominant cytokine combination identified in the reduced set yielded a classification accuracy of 80%, compared to 64% obtained when the entire cytokine profile was used without any feature selection technique. Discussion: A differential classification model to effectively differentiate the two categories of controlled and uncontrolled asthmatics was implemented and validated by identifying the predominant biomarkers from the available data panel. Conclusion: The performance evaluation results of the various classifiers using the predominant cytokine combination identified in the reduced set were carried out. An optimal mixing of both L1 and L2 regularizations was chosen by an empirical means for the elastic net. The ReliefF- based regularization technique used for predicting asthma control levels performed well with almost all regularization methods and performed extremely well with elastic net, yielding a lower error rate.