Predicting the Real-World Future of Glaucoma Patients? Cautions Are Required for Machine Learning

We read with great interest the recent article by Yuki et al. It is very thought-provoking that the authors predicted the likelihood of a future motor vehicle collision (MVC) among patients with primary open-angle glaucoma (POAG) based on multiple attributes using the penalized support vector machine (pSVM). However, several points remain to be discussed in terms of model design, data interpretation, as well as clinical application. On one hand, the design of the model is somewhat controversial (recapitulation in Fig. 1A). The highdimensional dataset (62 variables in the predpenSVM_basic model and 84 variables in the predSVM and predpenSVM_all models) is inclined to overfit when the sample size is small, because the cover range is not sufficient for pattern recognition on each attribute. Elaboration of attributes’ contribution is a promising alternative to address this issue. Model simplification (excluding the less contributed attributes) will broaden dramatically the application merit and be more cost-effective. Moreover, the progress of POAG and corresponding controlling efficacy have been reported to exhibit great heterogeneity. Thus, potential indictors regarding intraocular pressure, visual field, and drug intake should be included. Given that best-corrected visual acuity has a significant effect on the likelihood of MVC, other variables of visual function, including refractive conditions, stereoscopic vision, contrast sensitivity, and color vision, can improve the model performance as well. On the other hand, the model validation and its clinical merit should be interpreted cautiously (recapitulation in Fig. 1B). First concern lies in the consistency of predicted results among patients at the three involved centers. Differences should be evaluated carefully to investigate whether this predictive model could be extended to patients at other