Title: Fuzzy logic system for diabetic eye morbidity prediction

Authors: Tejas V. Bhatt; Raksha K. Patel; Himal B. Chitara; Gonçalo Marques; Akash Kumar Bhoi

Addresses: Department of Biomedical Engineering, Ganpat University - U.V. Patel College of Engineering, Gozaria, Gujarat 384012, India ' Department of Biomedical Engineering, Ganpat University - U.V. Patel College of Engineering, Gozaria, Gujarat 384012, India ' Department of Biomedical Engineering, Ganpat University - U.V. Patel College of Engineering, Gozaria, Gujarat 384012, India ' Instituto de Telecomunicações, Universidade da Beira Interior, 6201-001 Covilhã, Portugal ' Department of Electrical and Electronics Engineering, Sikkim Manipal Institute of Technology (SMIT), Sikkim Manipal University (SMU), Majitar 737136, Sikkim, India

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.

Keywords: artificial intelligence; blood sugar; cholesterol; diabetic retinopathy; fuzzy set theory.

DOI: 10.1504/IJCAT.2020.112680

International Journal of Computer Applications in Technology, 2020 Vol.64 No.4, pp.339 - 348

Received: 29 Apr 2020
Accepted: 09 May 2020
Published online: 28 Jan 2021 *

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