A Statistical Study to Analyze the Impact of External Weather Change on Chronic Pulmonary Infection in South Norway with Machine Learning Algorithms

In this paper, we analyzed the holistic impact of external weather on chronic pulmonary infection in the Agder region with traditional machine learning algorithms. Millions of people are diagnosed with Chronic Obstructive Pulmonary Disease (COPD). Our study is dedicated in the Agder region, the Southern part of Norway. Norway has four seasons – winter (December-February), late winter/spring (March-May), Summer (June-August), and Autumn (September-November) in a year with average annual temperature approx. 7.5 °C | 45.5 °F and an annual rainfall of 1260 mm or 49.6 in. in Kristiansand. As predicted by the World Health Organization (WHO), in 2016, Norway suffered from 8% mortality due to c(1)hronic respiratory diseases. The disease is strongly afflicted by meteorological and environmental factors in distinct regions worldwide. This article explores correlation and dependency between COPD and temperature, barometric pressure, and humidity in the Agder region. We relate multiple prognostic models for patient evaluation based on patient condition and weather information. The COPD data were accumulated from 94 patients that reside in the Agder region as part of a major EU-funded project called “United for Health”. Outcomes showed that there is a dependence between air temperature and the patient’s condition. However, there is no significant dependency between air pressure and humidity, and the patient assessment results in that region. The results help to predict the condition of COPD patients in south Norway based on the weather forecast.