Title: A cough type chronic disease prediction scheme using machine learning and diagnosis support system using a mobile application

Authors: Upol Chowdhury; Mahfuzulhoq Chowdhury

Addresses: Computer Science and Engineering Department, Chittagong University of Engineering and Technology, Chittagong – 4349, Bangladesh ' Computer Science and Engineering Department, Chittagong University of Engineering and Technology, Chittagong – 4349, Bangladesh

Abstract: Machine learning has been found to considerably lower the probability of inaccurate diagnoses when incorporated into modern diagnostic procedures. Different from the literature works, this paper proposes a method for diagnosing the three most similarly symptomised cough-type chronic diseases: COPD, bronchial asthma, and pneumonia. The symptoms of cough-type chronic disease patients admitted to the hospital were collected from eight medical colleges spread out over Bangladesh to construct the classifying model. This paper proposes a set of 27 attributes for appropriately classifying cough-type chronic disease instances. Several machine learning methods are tested using the dataset. Our research suggests gradient tree boosting to be the most effective, with a classification accuracy of 91%, despite the fact that previous research has identified support vector machine and random forest to be the most efficient models in these kinds of classification tasks. This paper also developed a mobile application for the diagnostic support systems.

Keywords: cough type chronic disease; machine learning; prediction; evaluation; healthcare; mobile application.

DOI: 10.1504/IJEH.2023.135798

International Journal of Electronic Healthcare, 2023 Vol.13 No.3, pp.209 - 230

Received: 31 Oct 2022
Accepted: 04 Sep 2023
Published online: 05 Jan 2024 *

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