Journal of Health & Medical Informatics

ISSN: 2157-7420

Open Access

Pushpalatha MP


  • Research Article
    A Comparative Performance Evaluation of Hybrid and Ensemble Machine Learning Models for Prediction of Asthma Morbidity
    Author(s): Pooja MR and Pushpalatha MPPooja MR and Pushpalatha MP

    One of the chronic respiratory diseases that affect a large proportion of the population is Asthma. Asthma is more prevalent in children of age groups 6-14 years. Early identification of the risk factors is an important intervention towards the management of the disease as the disease is progressive in nature. In our work, we assess the performance of the two machine learning approaches with respect to their accuracy in predicting the outcome of asthma disease after identifying the critical risk factors that help in the prognosis of the disease. We perform an empirical analysis of ensemble and hybrid machine learning models to deduce the best performing approach for the prediction of the outcome of asthma. The Neyveli rural asthma dataset of India, representing cross sectional study data gathered through questionnaires formulated under ISAAC study was used.. Read More»

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