5TH ABC Challenge: Forecasting Thermal Comfort Sensations for Heatstroke Prevention: Leveraging Physiological Data for Better Outcomes

Heatstroke prevention is crucial because heatstroke is a life-threatening condition that can result in severe health consequences and even death. Heatstroke occurs when the body's internal temperature rises to a dangerous level, usually as a result of prolonged exposure to high temperatures or physical exertion in hot weather. Symptoms of heatstroke include confusion, rapid heartbeat, rapid breathing, seizures, and loss of consciousness. Furthermore, the mortality rate due to overheating is estimated to increase by 260% by the 2050s. So, it is important to understand heatstroke symptoms in advance. Thus, forecasting the danger situation for heatstroke using physiological data and machine learning will be helpful because it can help identify individuals who are at risk for heatstroke before they develop symptoms. By analyzing physiological data such as heart rate, body temperature, and blood pressure, machine-learning algorithms can detect patterns and trends that may indicate an increased risk of heatstroke.  In this challenge, participants are required to forecast the personal thermal comfort sensations by using physiological data. The training dataset comprises timestamped observations for 23 individuals over a span of 6 days. Each observation consists of a specific feature value at a particular time. Your goal is to create a machine-learning model that can forecast the thermal sensations based on this historical data.