Sinergi
Vol 28, No 1 (2024)

Early detection of diabetes potential using cataract image processing approach

Moh. Khairudin (Department of Electrical Engineering, Faculty of Engineering, Universitas Negeri Yogyakarta)
Rendy Mahaputra (Department of Electrical Engineering, Faculty of Engineering, Universitas Negeri Yogyakarta)
Wiharto Wiharto (Department of Informatics Engineering, Faculty of Engineering, Universitas Muhammadiyah Surakarta)
Yasmin Mufidah (Department of Informatics Engineering, Faculty of Engineering, Universitas Negeri Sebelas Maret)
Leo Anang Miftahul Huda (Department of Informatics Engineering, Faculty of Engineering, Politeknik Negeri Jakarta)
Rafif Apta Reswara (Department of Informatics Engineering, Faculty of Engineering, Universitas Negeri Sebelas Maret)
Adelia Putri Nur Ahni (Department of Informatics Engineering, Faculty of Engineering, Universitas Negeri Sebelas Maret)
Gita Juli Hartanti (Department of Informatics Engineering, Faculty of Engineering, Universitas Negeri Sebelas Maret)



Article Info

Publish Date
15 Dec 2023

Abstract

Diabetes is a disease characterized by a high level of sugar in the blood. The disease occurs because of a disruption in the metabolic system when insulin is not produced effectively and functions properly. High blood sugar levels, for an extended period of time, can harm a few organ systems, including the heart and kidneys. Moreover, it may cause blindness or death if it is not carefully monitored. Because diabetes symptoms are rarely seen, one of the factors that may cause diabetes is self-awareness. Thus, with Artificial Intelligence, this problem can be solved. Artificial intelligence studies how machines can function like humans. This study implemented a Convolutional Neural Network algorithm with (1) input layer, (2) feature learning layer, (3) classification layer, and (4) output layer as the architecture for AI. The accuracy of the developed AI model was measured from its precision, recall, and f1-score. The results show that the model obtained 90% precision, recall, and f1-score for real-world cases found in two hospitals located in Solo and Yogyakarta, Indonesia. According to the results of the tests, 9 out of 10 patients were correctly predicted as having a high risk of diabetes based on their eye images.

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Journal Info

Abbrev

sinergi

Publisher

Subject

Civil Engineering, Building, Construction & Architecture Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

Description

SINERGI is a peer-reviewed international journal published three times a year in February, June, and October. The journal is published by Faculty of Engineering, Universitas Mercu Buana. Each publication contains articles comprising high quality theoretical and empirical original research papers, ...