Results: This study showed that only 25.2% of our respondents were aware of glaucoma and it is associated with ethnicity, religion, education, and household income. Besides, among those who were aware, they fall into the group of poor knowledge of glaucoma. On the other hand, the knowledge of glaucoma was associated with occupation and the awareness of glaucoma by definition. The validated questionnaire was distributed and the data were analyzed by SPSS software using t-test, one-way ANOVA, and chi-square test.
Conclusion: Awareness and knowledge of glaucoma in this population is low. These findings suggest that there is a need for an efficient information and education strategy to be designed and conducted to increase the awareness and knowledge of glaucoma so that early detection can be made and effective management of individuals with this condition can be delivered.
Aim: To produce an accurate model of BCVA changes of postpterygium surgery according to its morphological characteristics by using the machine learning technique. Methodology. A retrospective of the secondary dataset of 93 samples of pterygium patients with different pterygium attributes was used and imported into four different machine learning algorithms in RapidMiner software to predict the improvement of BCVA after pterygium surgery.
Results: The performance of four machine learning techniques were evaluated, and it showed the support vector machine (SVM) model had the highest average accuracy (94.44% ± 5.86%), specificity (100%), and sensitivity (92.14% ± 8.33%).
Conclusion: Machine learning algorithms can produce a highly accurate postsurgery classification model of BCVA changes using pterygium characteristics.