عنوان المقالة:تسخير خوارزميات التعلم الآلي لاكتشاف عوامل خطر الإصابة بالجلوكوما Employing Machine Learning Algorithms to Discover Risk Factors of Glaucoma
The statistics of the World Health Organization indicated that blindness or loss of vision in one of the eyes is one of the many threats and challenges in the world due to the increasing number of people infected annually. One of the many serious eye diseases is glaucoma, which leads to the loss of vision in the affected eye. Most studies on glaucoma are based on clinical trials and thus lack insights into the risk factors of glaucoma; these factors could be obtained from larger number of doctors and patients and analyzed sufficiently. With the recent development of technology, the need of utilizing machine learning algorithms in the medical field has increased to achieve a better and accurate health care system. Thus, this study aims to effectively discover the hidden risk factors of glaucoma by machine learning algorithms using real data collected from doctors and patients. The author used one of decision tree algorithms so-called J48 and one of association rule algorithm so-called apriori to analyze the collected data. The study shows that some of the risk factors of glaucoma which have been proven by medical trials are also obtained by means of the two algorithms. Therefore, machine learning algorithms can be used to effectively determine causes of any disease. The results not only provide a novel method to discover the risk factors of glaucoma but also reveal new risk factors of glaucoma which have not been discovered previously by studies based on medical trials such as discovery the relation chronic constipation and stomach upset with glaucoma as reasons leading to glaucoma. This study will contribute to avoiding blindness and preventing it by determining the recognition factors of glaucoma.
تاريخ النشر
04/10/2021
الناشر
IEEE
رقم المجلد
رقم العدد
ISSN/ISBN
978-1-6654-1322-0
رابط DOI
https://doi.org/10.1109/PRAI53619.2021.9551082
الكلمات المفتاحية
pattern recognition , machine learning algorithms , health information , glaucoma , risk factors , bioinformatics
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