This study focuses on highly accurate prediction of the onset of type-2 diabetes. We investigated whether prediction accuracy can be improved by utilizing lab test data obtained from health checkups and incorporating health claim text data such as medically diagnosed diseases with ICD10 codes and pharmacy information. In a previous study, prediction accuracy was increased slightly by adding diagnosis disease name and independent variables such as prescription medicine. Therefore, in the current study we explored more suitable models for prediction by using state-of-the-art techniques such as XGBoost and long short-term memory (LSTM) based on recurrent neural networks. In the current study, text data was vectorized using word2vec, and the prediction model was compared with logistic regression. The results obtained confirmed that onset of type-2 diabetes can be predicted with a high degree of accuracy when the XGBoost model is used.

In this study, the use of alternative acoustic sensors in human-robot communication is investigated. In particular, a Non-Audible Murmur (NAM) microphone was applied in teleoperating Geminoid HI-1 robot in noisy environments. The current study introduces the methodology and the results of speech intelligibility subjective tests when a NAM microphone was used in comparison with using a standard microphone. The results show the advantage of using NAM microphone when the operation takes place in adverse environmental conditions. In addition, the effect of Geminoid's lip movements on speech intelligibility is also investigated. Subjective speech intelligibility tests show that the operator's speech can be perceived with higher intelligibility scores when operator's audio speech is perceived along with the lip movements of robots.