Glaucoma is a leading cause of blindness worldwide. The aim of the current study was to evaluate the impact of an educational programme on knowledge, beliefs, practices and expectations towards glaucoma and eye care among adolescent patients with glaucoma. A purposive sample of 50 patients with glaucoma aged 12-18 years, attending ophthalmology outpatient clinics in Cairo, Egypt, were given an educational programme focusing on information about glaucoma, correcting patients' incorrect beliefs and expectations, and demonstrations and retraining about eye care. A range of tools was used to assess patients before and after the programme: questionnaire, observation checklist, physical assessment sheet, childrens' depression scale, children manifest anxiety scale, self-esteem inventory and patients' expectations scale. The programme significantly improved patients' knowledge and beliefs about glaucoma and their practices and expectations concerning eye care. Innovative educational programmes about eye diseases are needed to improve patients' knowledge and practices.

In sectors like healthcare, having classification models that are both reliable and accurate is vital. Regrettably, contemporary classification techniques employing machine learning disregard the correlations between instances within data. This research, to rectify this, introduces a basic but effective technique for converting tabulated data into data graphs, incorporating structural correlations. Graphs have a unique capacity to capture structural correlations between data, allowing us to gain a deeper insight in comparison to carrying out isolated data analysis. The suggested technique underwent testing once the integration of graph data structure-related elements had been carried out and returned superior results to testing solely employing original features. The suggested technique achieved validity by returning significantly improved levels of accuracy.