The Hybrid Method of SOM Artificial Neural Network and Median Thresholding for Segmentation of Blood Vessels in the Retina Image Fundus

Wiharto,Esti Suryani,Murdoko Susilo 한국지능시스템학회 2019 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.19 No.4

Blood vessels in the retina of the eye are one important sign when making a diagnosis of hypertensive retinopathy. On the retina can be known several signs including tortuosity and arteriovenous ratio. Blood vessels mixed with a number of objects in the retina, the segmentation of blood vessels becomes a very interesting challenge because they have to separate blood vessels from a number of objects. This study aims to segmentation blood vessels using the main method of self-organizing maps artificial neural networks (SOMANN). The proposed segmentation method is divided into three stages, namely preprocessing, segmentation, and performance analysis. The preprocessing step is to improve image quality using the contrast-limited adaptive histogram equalization (CLAHE), median filter, and morphology. The segmentation stage uses the SOM-ANN algorithm combined with the mean or median thresholding. The performance parameters which are measured consist of sensitivity, specificity, and area under the curve (AUC). The test results using the dataset STARE and DRIVE show that the median thresholding is able to provide the best AUC performance compared to the mean thresholding. The proposed segmentation model is able to provide performance in the excellent category, with AUC values of 90.55% for the STARE dataset and 90.35% for the DRIVE.

Modification of a Density-Based Spatial Clustering Algorithm for Applications with Noise for Data Reduction in Intrusion Detection Systems

Wiharto,Aditya K. Wicaksana,Denis E. Cahyani 한국지능시스템학회 2021 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.21 No.2

Monitoring activity in computer networks is required to detect anomalous activities. This monitoring model is known as an intrusion detection system (IDS). Most IDS model developments are based on machine learning. The development of this model requires activity data in the network, either normal or anomalous, in sufficient amounts. The amount of available data also has an impact on the slow learning process in the IDS system, with the resulting performance sometimes not being proportional to the amount of data. This study proposes an IDS model that combines DBSCAN modification with the CART algorithm. DBSCAN modification is performed to reduce data by adding a MinNeighborhood parameter, which is used to determine the distance of the density to the cluster center point, which will then be marked for deletion. The test results, using the Kaggle and KDDCup99 datasets, show that the proposed system model is able to maintain a classification accuracy above 90% for 80% data reduction. This performance was also followed by a decrease in computation time, for the Kaggle dataset from 91.8 ms to 31.1 ms, while for the KDDCup99 dataset from 5.535 seconds to 1.120 seconds.

Use of Artificial Bee Swarm Optimization (ABSO) for Feature Selection in System Diagnosis for Coronary Heart Disease

Wiharto,Yaumi A. Z. A. Fajri,Esti Suryani,Sigit Setyawan The Korea Institute of Information and Commucation 2023 Journal of information and communication convergen Vol.21 No.2

The selection of the correct examination variables for diagnosing heart disease provides many benefits, including faster diagnosis and lower cost of examination. The selection of inspection variables can be performed by referring to the data of previous examination results so that future investigations can be carried out by referring to these selected variables. This paper proposes a model for selecting examination variables using an Artificial Bee Swarm Optimization method by considering the variables of accuracy and cost of inspection. The proposed feature selection model was evaluated using the performance parameters of accuracy, area under curve (AUC), number of variables, and inspection cost. The test results show that the proposed model can produce 24 examination variables and provide 95.16% accuracy and 97.61% AUC. These results indicate a significant decrease in the number of inspection variables and inspection costs while maintaining performance in the excellent category.

Hybrid Feature Selection Method Based on Genetic Algorithm for the Diagnosis of Coronary Heart Disease

Wiharto Wiharto,Esti Suryani,Sight Setywan,Bintang PE Putra 한국정보통신학회 2022 Journal of information and communication convergen Vol.20 No.1

Coronary heart disease (CHD) is a comorbidity of COVID-19; therefore, routine early diagnosis is crucial. A large number of examination attributes in the context of diagnosing CHD is a distinct obstacle during the pandemic when the number of health service users is significant. The development of a precise machine learning model for diagnosis with a minimum number of examination attributes can allow examinations and healthcare actions to be undertaken quickly. This study proposes a CHD diagnosis model based on feature selection, data balancing, and ensemble-based classification methods. In the feature selection stage, a hybrid SVM-GA combined with fast correlation-based filter (FCBF) is used. The proposed system achieved an accuracy of 94.60% and area under the curve (AUC) of 97.5% when tested on the z-Alizadeh Sani dataset and used only 8 of 54 inspection attributes. In terms of performance, the proposed model can be placed in the very good category.

Intelligence System for Diagnosis Level of Coronary Heart Disease with K-Star Algorithm

Wiharto Wiharto,Hari Kusnanto,,Herianto Herianto 대한의료정보학회 2016 Healthcare Informatics Research Vol.22 No.1

Objectives: Coronary heart disease is the leading cause of death worldwide, and it is important to diagnose the level of the disease. Intelligence systems for diagnosis proved can be used to support diagnosis of the disease. Unfortunately, most of the data available between the level/type of coronary heart disease is unbalanced. As a result system performance is low. Methods: This paper proposes an intelligence systems for the diagnosis of the level of coronary heart disease taking into account the problem of data imbalance. The first stage of this research was preprocessing, which included resampled non-stratified random sampling (R), the synthetic minority over-sampling technique (SMOTE), clean data out of range attribute (COR), and remove duplicate (RD). The second step was the sharing of data for training and testing using a k-fold cross-validation model and training multiclass classification by the K-star algorithm. The third step was performance evaluation. The proposed system was evaluated using the performance parameters of sensitivity, specificity, positive prediction value (PPV), negative prediction value (NPV), area under the curve (AUC) and F-measure. Results: The results showed that the proposed system provides an average performance with sensitivity of 80.1%, specificity of 95%, PPV of 80.1%, NPV of 95%, AUC of 87.5%, and Fmeasure of 80.1%. Performance of the system without consideration of data imbalance provide showed sensitivity of 53.1%, specificity of 88,3%, PPV of 53.1%, NPV of 88.3%, AUC of 70.7%, and F-measure of 53.1%. Conclusions: Based on these results it can be concluded that the proposed system is able to deliver good performance in the category of classification.

Interpretation of Clinical Data Based on C4.5 Algorithm for the Diagnosis of Coronary Heart Disease

Wiharto Wiharto,Hari Kusnanto,,Herianto Herianto 대한의료정보학회 2016 Healthcare Informatics Research Vol.22 No.3

Objectives: The interpretation of clinical data for the diagnosis of coronary heart disease can be done using algorithms in data mining. Most clinical data interpretation systems for diagnosis developed using data mining algorithms with a blackbox approach cannot recognize examination attribute relationships with the incidence of coronary heart disease. Methods: This study proposes a system to interpretation clinical examination results for the diagnosis of coronary heart disease based the decision tree algorithm. This system comprises several stages. First, oversampling is carried out by a combination of the synthetic minority oversampling technique (SMOTE), feature selection, and the C4.5 classification algorithm. System testing is done using k-fold cross-validation. The performance parameters are sensitivity, specificity, positive prediction value (PPV), negative prediction value (NPV) and the area under the curve (AUC). Results: The results showed that the performance of the system has a sensitivity of 74.7%, a specificity of 93.7%, a PPV of 74.2%, an NPV of 93.7%, and an AUC of 84.2%. Conclusions: This study demonstrated that, by using C4.5 algorithms, data can be interpreted in the form of a decision tree, to aid the understanding of the clinician. In addition, the proposed system can provide better performance by category.

Hilbert-Schmidt Independence Criterion Lasso Feature Selection in Parkinson’s Disease Detection System

Wiharto Wiharto,Ahmad Sucipto,Umi Salamah 한국지능시스템학회 2023 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.23 No.4