Retracted: Heart Abnormalities Detection Through Iris Based on Mobile

Kusuma, Febriana Dyah; Kusumaningtyas, Entin Martiana; Barakbah, Ali Ridho; Hermawan, Aditya Afgan

doi:10.1109/kcic.2018.8628515

Cited by 7 publications

(4 citation statements)

References 3 publications

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“…In this paper, they use Gray Level Co-Occurrence Matrix (GLCM) feature extraction and Bayesian regularization (BR) classification to diagnose either the subject is suffering from the disease or not. Arcus Senilis [13], [22][23][24][25][26][27] Autonomic Nerve [28] Brain [29] Heart [12], [16], [30][31] Kidney [32] Liver [33] Lung [4], [15], [34] Pancreas [3], [7], [8], [14], [35][36][37] Stomach [38] Iris Recognition System ROI and Accuracy…”

Section: Discussionmentioning

confidence: 99%

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Retracted: A Review of Iris Recognition System ROI and Accuracy

Halim

Emanuel

2020

2020 International Conference on Smart Technology and Applications (ICoSTA)

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Section: Discussionmentioning

confidence: 99%

“…This classifier usually used for black and white ratio feature extraction. The thresholding algorithm steps are as follow [16]:…”

Section: A Tresholding Algorithmmentioning

confidence: 99%

Retracted: A Review of Iris Recognition System ROI and Accuracy

Halim

Emanuel

2020

2020 International Conference on Smart Technology and Applications (ICoSTA)

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“…Putra et al [ 25 ] reached an accuracy value of 0.78 by using the Neural Network with the same feature extraction method and also achieved 90% success with the PCA method. Kusuma et al [ 27 ] and Permatasari et al [ 26 ] used the Black and White Ratio and PCA methods for feature extraction, respectively, and performed classification with the Thresholding and SVM methods, respectively. These studies did not include performance metrics other than accuracy.…”

Section: Resultsmentioning

confidence: 99%

“…The highest accuracy achieved was reported to be 80%. Kusuma et al [ 27 ] proposed a model for detecting cardiac abnormalities by acquiring and using iris images with a mobile-based system. The ratio of black and white pixels obtained after converting the analysis region to black and white format was used as a feature.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Coronary Artery Disease Using Machine Learning Techniques with Iris Analysis

2023

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Coronary Artery Disease (CAD) occurs when the coronary vessels become hardened and narrowed, limiting blood flow to the heart muscles. It is the most common type of heart disease and has the highest mortality rate. Early diagnosis of CAD can prevent the disease from progressing and can make treatment easier. Optimal treatment, in addition to the early detection of CAD, can improve the prognosis for these patients. This study proposes a new method for non-invasive diagnosis of CAD using iris images. In this study, iridology, a method of analyzing the iris to diagnose health conditions, was combined with image processing techniques to detect the disease in a total of 198 volunteers, 94 with CAD and 104 without. The iris was transformed into a rectangular format using the integral differential operator and the rubber sheet methods, and the heart region was cropped according to the iris map. Features were extracted using wavelet transform, first-order statistical analysis, a Gray-Level Co-Occurrence Matrix (GLCM), and a Gray Level Run Length Matrix (GLRLM). The model’s performance was evaluated based on accuracy, sensitivity, specificity, precision, score, mean, and Area Under the Curve (AUC) metrics. The proposed model has a 93% accuracy rate for predicting CAD using the Support Vector Machine (SVM) classifier. With the proposed method, coronary artery disease can be preliminarily diagnosed by iris analysis without needing electrocardiography, echocardiography, and effort tests. Additionally, the proposed method can be easily used to support telediagnosis applications for coronary artery disease in integrated telemedicine systems.

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