Retracted: Heart disorder detection based on computerized iridology using support vector machine

Permatasari, Lintang Indah; Novianty, Astri; Purboyo, Tito Waluyo

doi:10.1109/iccerec.2016.7814983

Cited by 11 publications

(7 citation statements)

References 4 publications

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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%

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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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Section: Resultsmentioning

confidence: 99%

“…They achieved a classification accuracy of 77.5% for the test data using GLCM features, and they achieved 90% accuracy using PCA features. The PCA feature extraction method and SVM classifier were utilized in the method proposed by Permatasari et al [ 26 ]. The highest accuracy achieved was reported to be 80%.…”

Section: Introductionmentioning

confidence: 99%

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

2023

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“…The performance of the naive Bayes classifier was better than others and achieved a 61.96% diagnostic accuracy. Permatasari et al [27] proposed a method of heart condition detection from iris classification using the support vector machine (SVM). Feature extraction was done by using a principal component analysis (PCA) method.…”

Section: Related Workmentioning

confidence: 99%

Classification of Body Constitution Based on TCM Philosophy and Deep Learning

Aslam

Yang

et al. 2020

Symmetry

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There is a growing demand for alternative or complementary medicine in health care disciplines that uses a non-invasive instrument to evaluate the health status of various organs inside the human body. In this regard, we proposed a real-time, non-invasive, and painless technique to assess an individual’s health condition. Our approach is based on the combination of iridology and the philosophy of traditional Chinese medicine (TCM). The iridology chart presents perfect symmetry between the left and right eyes, and such a unique representation reveals the body constitution based on TCM philosophy, which classifies the aforementioned body constitution into a combination of nine categories to describe the varieties of genomic traits. In addition, we applied a deep-learning method along with the combination of iridology and TCM to predict the possible physiological or psychological strength or weakness of the subjects and give advice to them about how to take care of their health according to the body constitution assessment. We used several pre-trained convolutional neural networks (CNNs, or ConvNet), such as a residual neural network (ResNet50), InceptionV3, and dense convolutional network (DenseNet201), to classify the body constitution using iris images. In the experiments, the CASIA-Iris-Thousand database was used to perform this task. The experimental results showed that the proposed iris-based health assessment method achieved an 82.9% accuracy.

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“…Iridologi adalah diagnosis kondisi medis prapenyakit melalui kelainan pigmentasi pada iris. Iridologi dapat mengetahui kondisi organ pada sistem dalam tubuh melalui karakteristik atau tanda-tanda yang ada di iris (Dewi, Novianty, and Purboyo 2017) dan sebagai alternatif pemeriksaan medis untuk mendeteksi penyakit atau gangguan pada organ tertentu melalui pengamatan warna (Permatasari, Novianty, and Purboyo 2017). Gambar 1 memperlihatkan bagan Iridologi dari Dr. Bernard Jensen.…”

Section: A Iridologiunclassified

Sistem Diagnosis Otomatis Identifikasi Penyakit Jantung Coroner Menggunakan Ektraksi Ciri GLCM dan Klasifikasi SVM

Gunawan

Fitriani

Putra

2020

JIM

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Jantung coroner merupakan salah satu penyakit jantung yang sangat banyak menyerang manusia. Penyebab jantung coroner adalah terjadinya penumpukan lemak dan kolestrol pada dinding pembuluh darah. Penyebab utama dari jantung coroner karena gaya hidup manusia yang kurang sehat. Pola makan tidak yang buruk, obesitas dan kurang berolahraga menjadi penyebab utama munculnya penyakit jantung coroner. Penyempitan pembuluh darah sangat berkaitan erat dengan aliran darah yang terjadi ke mata khususnya bagian iris. Sehingga diperlukannya suatu cara dengan metode yang lebih efesien dan murah untuk mengetahui dan mengidentifikasi penyakit jantung sejak diri dengan menggunakan iridologi. Sistem yang dirancang dengan menggunakan Circle Hough Tranform (CHT) sebagai deteksi bagian iris secara otomatis, Gray Level Co- occurrence Matrix (GLCM) sebagai ekstraksi ciri dan Support Vector Machine (SVM) sebagai klasifikasi. Pengujian telah dilakukan terhadap 40 data citra yang memperoleh tingkat keberhasilan identifikasi sebesar 87.5%.

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Retracted: Heart disorder detection based on computerized iridology using support vector machine

Cited by 11 publications

References 4 publications

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

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

Classification of Body Constitution Based on TCM Philosophy and Deep Learning

Sistem Diagnosis Otomatis Identifikasi Penyakit Jantung Coroner Menggunakan Ektraksi Ciri GLCM dan Klasifikasi SVM

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