Heat Stroke Risk Detection Using Machine Learning

Extreme hot temperatures in Malaysia due to climate change has increase the risk of heat-related health issues. It is particularly for outdoor laborers and vulnerable group such as the elderly and those with chronic illnesses. The phenomenon which indicates a 2.7 to 4.0 ℃ increase every century has contributes to numbers of conditions from heat exhaustion to heat stroke. This situation is imposing a significant pressure on healthcare systems and highlighting the necessity for prevention measures. As heat stroke is a significant danger to health, especially during periods of extreme hot weather. It may lead to multiple medical conditions and perhaps life-threatening problems if not appropriately diagnosed and treated. Thus, the goal of this study is to propose a heat stroke risk detection application that able to help detecting heat stroke risk among user. This application is developed with the implementation of a machine learning that able to detect heat stroke based on users’ medical conditions. Through the application, it enables an early heat stroke risk detection and give suggestions on actions need to be taken to prevent severe heat stroke consequences with the implementation of random forest method.