Predicting Success in Descemet Membrane Endothelial Keratoplasty Surgery Using Machine Learning.

PURPOSE: This study aimed to predict early graft failure (GF) in patients who underwent Descemet membrane endothelial keratoplasty based on donor characteristics. METHODS: Several machine learning methods were trained to predict GF automatically. To predict GF, the following variables were obtained: donor age, sex, systemic diseases, medications, duration of stay in the intensive care unit, death-to-preservation time (DPT), endothelial cell density of the cornea, tightness of Descemet membrane roll during surgery, anterior chamber tamponade, tamponade used for rebubbling, and preoperative best corrected visual acuity. Five classification methods were experimented with the study data set: random forest, support vector machine, k-nearest neighbor, RUSBoosted tree, and neural networks. In holdout validation, 75% of the data were used in training and the remaining 25% used in testing. The predictive accuracy, sensitivity, specificity, f-score, and area under the receiver operating characteristic curve of the methods were evaluated. RESULTS: The highest classification accuracy achieved during the experiments was 96%. The precision, recall, and f1-score values were 0.95, 0.81, and 0.90, respectively. Feature importance was also computed using analysis of variance. The model revealed that GF risk was related to DPT and the intensive care unit duration (P < 0.05). No significant relationship was found between donor age, endothelial cell density, systemic diseases and medications, graft roll, tamponades, and GF risk. CONCLUSIONS: This study shows a strong relationship between increased intensive care duration, DPT, and GF. Experimental results demonstrate that machine learning methods may effectively predict GF automatically.

The effect of CLOSER-computer-based exercise program in older adults with a history of falls: A pilot study.

CLOSER is a computer-based exercise program that aims to improve older adults' health, fitness and social lives. This pilot study aimed to examine the effect of CLOSER, the first computer-based exercise program developed for older adults on a national scale, on those with a history of falls. Forty-eight older adults (71.33 ± 7.47) with a history of falling at least once in the last year were included in the study. Older adults performed CLOSER exercises for (balance maintenance, neck rotation, rhythmic walking, knee flexion and trunk rotation) 2 sessions per week for eight weeks. All individuals were evaluated at baseline and the end of the eighth week. The primary outcome measures were the 30-s Chair-Stand Test (p = 0.002), the Berg Balance Scale (p = 0.002), the Falls Efficacy Scale International (p = 0.003), the Timed Up and Go Test (p = 0.008) and the motivation level (p = 0.007) statistically significant improvements were observed. The results show that a CLOSER-computer-based exercise program effectively increases balance and reduces the risk and fear of falling. In the future, CLOSER could significantly contribute to the healthcare system as an alternative aid for home-based exercise.

Slime Mould Algorithm: A Comprehensive Survey of Its Variants and Applications.

Meta-heuristic algorithms have a high position among academic researchers in various fields, such as science and engineering, in solving optimization problems. These algorithms can provide the most optimal solutions for optimization problems. This paper investigates a new meta-heuristic algorithm called Slime Mould algorithm (SMA) from different optimization aspects. The SMA algorithm was invented due to the fluctuating behavior of slime mold in nature. It has several new features with a unique mathematical model that uses adaptive weights to simulate the biological wave. It provides an optimal pathway for connecting food with high exploration and exploitation ability. As of 2020, many types of research based on SMA have been published in various scientific databases, including IEEE, Elsevier, Springer, Wiley, Tandfonline, MDPI, etc. In this paper, based on SMA, four areas of hybridization, progress, changes, and optimization are covered. The rate of using SMA in the mentioned areas is 15, 36, 7, and 42%, respectively. According to the findings, it can be claimed that SMA has been repeatedly used in solving optimization problems. As a result, it is anticipated that this paper will be beneficial for engineers, professionals, and academic scientists.