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All Journal International Journal of Advances in Applied Sciences TELKOMNIKA (Telecommunication Computing Electronics and Control) Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Indonesian Journal of Electrical Engineering and Computer Science
Wiharto Wiharto
Sebelas Maret University
Computer Science & IT Earth & Planetary Sciences Electrical & Electronics Engineering Environmental Science Materials Science & Nanotechnology Mathematics Physics

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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.
A Tiered Approach On Dimensional Reduction Process for Prediction of Coronary Heart Disease Wiharto Wiharto; Herianto Herianto; Hari Kusnanto
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 2: August 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v11.i2.pp487-495

Abstract

The use of dimensional reduction in the diagnostic system model of coronary heart disease, many same of case do not take into account the clinical procedures commonly used by clinicians in diagnosis. This requires that the examination be done thoroughly, thus making the high cost of diagnosis. This study aims to develop a tiered approach model in reducing dimensions for predicting CHD. The method in this research is divided into several stages, namely preprocessing, building the knowledge base and system testing. Preprocessing consists of several processes, namely the removal of missing value data, grouping attributes, and dividing data for training and testing. Knowledge base modeling is divided into three levels. The first level were the risk factor attributes, the second level were the type of chest pain & ECG, and the third were scintigraphy & coronary angiography. The knowledge base was modeled based on fuzzy rules and its inferencing process using Mamdani method. The first, fuzzy rule-based was obtained by using the FRS study. The second and third stage, using the induction rule algorithm to get the rule, then converted to fuzzy rule. The tested algorithm were C4.5, CART, and FDT. The system testing was performed by the 5-folds cross-validation method, with performance parameters based on population and individual. The test resulted using the Cleveland and Hungarian datasets, the FRS+CART combination was capable of reducing the most attributes and the highest likelihood ratio performance parameter, which was 15.96. FRS+C4.5, at least the attributes were reduced, but has an AUC performance of 80.43%, while FRS+FDT, more reduced attributes than FRS+C4.5, and AUC performance parameters are better than FRS+CART. Dimensional reduction model for prediction of CHD, capable of providing better performance than not tiered.
Early detection of coronary heart disease based on risk factors using interpretable machine learning Wiharto Wiharto; Farah Nada Mufidah
International Journal of Advances in Applied Sciences Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v13.i4.pp944-956

Abstract

Coronary heart disease (CHD) is the leading cause of death in the world. The risk of coronary heart disease can be reduced or even prevented by early detection. Early detection of CHD has been widely developed using machine learning, but the machine learning algorithms used sometimes have low interpretability. Low interpretability makes it difficult for users to understand the cause of the decision. Referring to this, this research aims to propose an early detection model using machine learning interpretability, which is implemented using the C5.0 algorithm and interpreted using Shapley additive explanations (SHAP). This research method is divided into 3 stages, namely preprocessing, interpretable machine learning, and performance evaluation. This study used 215 patient data from Dr. Moewardi Surakarta Hospital. Testing the resulting model using the k-folds cross-validation method. The test results show that the risk factors that make a high contribution to the output of the coronary heart disease detection model are systolic blood pressure, diastolic blood pressure, and employment level, with the resulting accuracy performance of 84.64%. The proposed model can be an alternative for early prediction of coronary heart disease which can explain the influence of each selected risk factor on the model output.
Co-Authors Esti Suryani Farah Nada Mufidah Hari Kusnanto Herianto . Muhammad Y. Kipti Vicka Cahyawati