Open AccessDiabetes and Heart Disease Identification System Using Iris on the Healthcare Kiosk
Author(s) -
Entin Martiana Kusumaningtyas,
Ali Ridho Barakbah,
S. Danggriawan
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1811/1/012096
Subject(s) - diabetes mellitus , medicine , heart disease , pancreas , population , identification (biology) , computer science , artificial intelligence , environmental health , endocrinology , biology , botany
Data reported by World Life Expectancy said, as many as 9.89% of Indonesia’s population died due to suffering from heart disease and as much as 7.18% due to suffering from diabetes. The heart and pancreas are very important organs for the human body. The heart has a function to flow blood to all parts of the body. While the pancreas is the organ responsible for regulating insulin levels. Organs in the human body can also be damaged so that it can inhibit the work process of these organs. Damage to the pancreas causes diabetes. To find out whether a person has diabetes or heart disease, it is necessary to carry out time-consuming and expensive laboratory tests. In this study, we propose an identification system for diabetes and heart disease using irises on Healthcare Kiosk. The method used in detecting diseases of the body through the iris is called iridology. This identification system will be in the form of a desktop application that can be used at Healthcare Kiosk. The stages carried out in this study were photographing the patient’s left eye using a special camera called an Eyeronec, target-based cropping, preprocessing, auto-cropping using integral projection, auto-cropping to remove sclera, taking pancreatic and heart ROI, feature extraction and classification. Auto cropping shows results 60% successful, 33% scant, and 7% failed. The classification process was carried out by training 31 training data that was labeled normal or abnormal by iridology experts. In this study, the system testing accuracy was 83.87% for diabetes and 80.65% for heart disease.