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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
Esti Suryani
Sebelas Maret University
Computer Science & IT Electrical & Electronics Engineering

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Assessment of Early Hypertensive Retinopathy using Fractal Analysis of Retinal Fundus Image Wiharto Wiharto; Esti Suryani; Muhammad Y. Kipti
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 1: February 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v16i1.6188

Abstract

Hypertensive retinopathy is characterized by changes in retinal vessels, a change known as tortuosity. Automated analysis of retinal vascular changes will make it easier for clinicians to make an initial diagnosis of the disease. The pattern of blood vessels in the retina of the eye can be approached with a fractal pattern. This study proposes a method for the early detection of disease hypertensive retinopathy using the fractal analysis approach fundus retinal image. Variable fractal used is the fractal dimension and lacunarity, whereas for the classification algorithm using ensemble Random Forest and validation using the k-fold cross-validation. Performance measurement using the parameters of accuracy, positive prediction value (PPV), negative prediction value (NPV), sensitivity, specificity and area under the curve (AUC). The test results using 10-fold cross-validation values obtained accuracy 88.0%, PPV 84.0%, NPV 92.0%, sensitivity 91.3%, specificity 85.19%, and 88.25% AUC. The performance is produced when using lacunarity the box size 22. Based on the research results, it can be concluded that early detection of hypertensive retinopathy with fractal analysis approaches have a performance based on AUC produced included in good categories.
The methods of duo output neural network ensemble for prediction of coronary heart disease Wiharto Wiharto; Esti Suryani; Vicka Cahyawati
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 7, No 1: March 2019
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (318.734 KB) | DOI: 10.52549/ijeei.v7i1.458

Abstract

The occurrence of Coronary heart disease (CHD) is hard to predict yet, but the assessment of CHD risk for the next ten years is possible. The prediction of coronary heart disease can be modelled using multi-layer perceptron neural network (MLP-ANN). Prediction model with MLP-ANN has either positive or negative CHD output, which is a binary classification. A prediction model with binary classification requires determination of threshold value before the classification process which increases the uncertainty in the classification process. Another weakness of the MLP-ANN model is the presence of overfitting. This study proposes a prediction model for coronary heart disease using the duo output artificial neural network ensemble (DOANNE) method to overcome the problems of overfitting and uncertainty of classification in MLP-ANN. This research method was divided into several stages, namely data acquisition, pre-processing, modelling into DOANNE, neural network ensemble training with Levenberg-Marquard (LM) algorithm, system performance testing, and evaluation. The results of the study showed that the use of DOANNE-LM method was able to provide a significant improvement from the MLP-ANN method, indicated by the results of statistical tests with p-value <0.05.
Co-Authors Muhammad Y. Kipti Vicka Cahyawati Wiharto Wiharto