Paper Contents
Abstract
Climate change is becoming a menace to our human health particularly among themost vulnerable groups of people: the elderly, children and people with chronicailments. To overcome this problem, we built another AI-based health monitoringsystem. This system is aimed at forecasting health risks associated with the climateby means of integrating data about the environment such as temperature, humidityand quality of air with the personal health of an individual. Structured data sets ofthe model parameters were made using State, District, Year, Month, Temperature (oC), Humidity (per cent), Rainfall (mm), Disease occurrence, Affected number ofChildren, Affected number of Elders and Population. The system issues early alertsand insightful information about the person using machine learning to aid inpreventative health services and improved responses to weather-related disasters. We have done experiments with various algorithms to determine which one workedbest in our study. Random Forest algorithm was the best as it gave 93% accuracy inrisk prediction. This was then succeeded by Logistic Regression of 90% andSupport Vector Machine (SVM) of 88%. Such findings demonstrate that AI couldbe an effective instrument to enhance health outcomes, decrease the climate- sensitivity of diseases burden and assist policy-makers to better distribute resourcesto the most vulnerable groups. Index terms - AI, Health Monitoring, Climate Change, Vulnerable Populations, Predictive Analytics, Machine Learning
Copyright
Copyright © 2025 Deepa Jaiswal. This is an open access article distributed under the Creative Commons Attribution License.
