Automatic prediction of visual discomfort for stereoscopic videos is important to address the viewing safety issues in stereoscopic displays. In literature, disparity and motion characteristics have been known as major factors of visual discomfort caused by stereoscopic content. For developing an algorithm to accurately predict visual discomfort in stereoscopic videos, this study investigates how to combine the individual prediction values of disparity- and motion-induced discomforts into an overall prediction value of visual discomfort. To that end, subjective experiments were performed with various stereoscopic videos, and four possible combination methods were compared based on the results of subjective experiments. The combination methods used in this study were the weighted summation, multiplication, Minkowski summation, and max combination methods. Experimental results showed that the Minkowski summation combination with a high exponent and max combination yielded the best accuracy in predicting the overall level of visual discomfort. The results indicated that the overall level of perceived discomfort could be dominantly affected by the most significant discomfort factor, i.e., the winner-takes-all mechanism. Our results could be useful for the future development of an accurate and reliable algorithm for visual discomfort prediction.