Rohaida Ismail

Abstract Traditional statistical models often struggle to accurately predict global burden of respiratory diseases due to the complex and interdependent nature of environmental variables. To address these challenges, this study aims to develop and evaluate machine learning models for respiratory disease prediction from 1999 to 2015 in Penang, Malaysia.

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All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.