Using evolutionary rough sets on stress prediction model by biomedical signal

Mental stress has been proved to play an important role in civilization diseases; how to improve the quality of diagnosis has become an important task. In this paper, we propose a hybrid evolutionary approach, RS-HTGA, to extract knowledge to support the physicians´ decision-making. The proposed method has been successfully applied to metal stress biomedical signal diagnosis and clinical data sets. The results show that the proposed method can not only effectively extract the decision rules without external information or prior knowledge, but also allowed expert reasoning. The experimental results also show that the model can achieve a higher level of accuracy (overall accuracy of 60%, coverage of 100%).