Fuzzy logic system for diabetic eye morbidity prediction. (11th January 2021)

• Record Type: Journal Article
• Authors: Bhatt, Tejas V.; Patel, Raksha K.; Chitara, Himal B.; Marques, Gonçalo; Bhoi, Akash Kumar
• Is Part Of: International journal of computer applications technology. Volume 64:Number 4(2020)
• Page Start: 339
• Page End: 348
• Publication Date: 2021-01-11
• Subjects: artificial intelligence -- blood sugar -- cholesterol -- diabetic retinopathy -- fuzzy set theory; Technology -- Data processing -- Periodicals; 620.00285
• Languages: English
• ISSNs: 0952-8091

Abstract

Diabetes is a common chronic disease; the number of people who are affected by this health problem is increasing worldwide, leading to a high cost for healthcare systems. Therefore, the main contribution of this paper is to present a fuzzy logic system for diabetic eye morbidity prediction. This work is divided into two parts. The first part is the examination of eye vision by the ophthalmologist and also other examinations such as postprandial blood sugar, hypotension, cholesterol, and duration of diabetes. The second part is the analysis of 400 patients' medical records collected in 2019. The fuzzy system proposed for prediction of diabetes retinopathy provides reliable accuracy for eye vision-threatening and eye morbidity. The proposed fuzzy system has five input parameters and one output parameter, which predicts diabetic neuropathy. The input parameters are random blood sugar, postprandial blood sugar, hypotension, cholesterol, and eye vision. The output parameter is the morbidity in diabetic retinopathies, which are non-proliferative, proliferative, clinically significant macular oedema. The proposed system is designed to support the endocrinologist and ophthalmologist in the diagnosis of diabetic retinopathy.