Functional analysis of glaucoma data.

We refer glaucoma to a category of eye disorders often associated with a dangerous buildup of intraocular pressure (IOP), which can damage the eyes' optic nerve that transmits visual information to the brain. Because IOP changes over time, it is a function of time, and it is an advantage that we analyze the phenomenon using functional data analysis. In this paper, we treat the data related to the IOP of 35 patients with right eye glaucoma, collected in Rasul-e-Akram Hospital at Tehran, Iran, over the years 2007–2011. We shall explore the structure of the data in search of the features that describe them, and find the characteristics that give a comprehensible presentation of the structure of the variability in the data.We extract patterns of variation in the data by using a generalization of the smoothed functional principal component analysis to obtain the main factors causing glaucoma and then determine their importance. We also explore the correlation patterns between the IOP of right and left eyes, and then model the left eye IOP of the glaucoma patients at each time on the basis of their right eye IOP in a previous interval of time.We can use the model to predict the values of the former variable by using the latter one in a previous time interval.