In order to detect a person's individual momentary thermal sensation and comfort, an increased number of models are developed that involve physiological data, especially skin temperatures and features based on the heart activity. In this paper, we investigate the feasibility of Machine Learning (ML) models which include physiological data based on two data sources: (1) a smartwatch and (2) a portable chest belt device. Further, we investigate the difference between thermal sensation and comfort votes and propose a new combined ground truth label. Data were collected in lab-like studies in a single-bed hotel room. Our study focuses on the detection of cold-induced thermal discomfort by varying the room air temperature between 24 °C and 20 °C. Results show that ML based approaches lead to accuracies up to 83.1% when using a chest belt device. Using a smartwatch, accuracies drop down to 79.8%. Our investigation shows that even though Heart Rate (HR) based features improve the prediction accuracy, the highest feature importances have distal skin temperature features.