This study assessed the feasibility of five separate machine learning (ML) classifiers for predicting disease progression in patients with pre-dialysis chronic kidney disease (CKD). The study enrolled 858 patients with CKD treated at a veteran’s hospital in Taiwan. After classification into early and advanced stages, patient demographics and laboratory data were processed and used to predict progression to renal failure and important features for optimal prediction were identified. The random forest (RF) classifier with synthetic minority over-sampling technique (SMOTE) had the best predictive performances among patients with early-stage CKD who progressed within 3 and 5 years and among patients with advanced-stage CKD who progressed within 1 and 3 years. Important features identified for predicting progression from early- and advanced-stage CKD were urine creatinine and serum creatinine levels, respectively. The RF classifier demonstrated the optimal performance, with an area under the receiver operating characteristic curve values of 0.96 for predicting progression within 5 years in patients with early-stage CKD and 0.97 for predicting progression within 1 year in patients with advanced-stage CKD. The proposed method resulted in the optimal prediction of CKD progression, especially within 1 year of advanced-stage CKD. These results will be useful for predicting prognosis among patients with CKD.

The optical power density in biotissue is an important issue for photobiomodulation (PBM) clinical applications. In our previous study, the maximal dose and the power density distributions of 830 nm lasers under human skin could be exactly calculated and measured. In this work, the laser power density in tissue can be changed by adjusting the focal point of the lens. From the experimental results, it is evident that the power densities on the attached gingiva and the surrounding tissues can be improved. Thus, the dose of a near-infrared (NIR) laser in the target tissue can be increased with a suitable lens. Most importantly, focusing lasers on deeper tissue can avoid any damage to the skin. This study provides a dose optimization method on the target tissue, and the results can be applied to clinical applications, especially laser acupuncture (LA).