Using Machine Learning in the Prediction of the Influence of Atmospheric Parameters on Health

Dragan Ranđelović (), Milan Ranđelović and Milan Čabarkapa
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Dragan Ranđelović: Faculty of Diplomacy and Security, University Union-Nikola Tesla Belgrade, 11000 Belgrade, Serbia
Milan Ranđelović: Science Technology Park, 18000 Niš, Serbia
Milan Čabarkapa: Faculty of Electrical Engineering, University of Belgrade, 11000 Belgrade, Serbia

Mathematics, 2022, vol. 10, issue 17, 1-30

Abstract: Technological development has brought humanity to the era of an information society in which information is the main driver. This implies existing large amounts of data from which knowledge should be extracted. In this sense, artificial intelligence represents a trend applied in many areas of human activity. This paper is focused on ensemble modeling based on the use of several machine learning algorithms, which enable the prediction of the risk to human health due to the state of atmospheric factors. The model uses two multi-agents as a technique of emergent intelligence to make a collective decision. The first agent makes a partial decision on the prediction task by learning from the available historical data. In contrast, the second agent does the same from the data available in real-time. The proposed prediction model was evaluated in a case study related to the city of Niš, Republic of Serbia, and showed a better result than each algorithm separately. It represents a reasonable basis for further upgrading both in the scope of different groups of the atmospheric parameters and in the methodological sense, as well as technically through implementation in a practical web citizen service.

Keywords: machine learning; regression; classification; decision trees; prediction; atmospheric parameters; health; emergent intelligence (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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