A study of data randomization on a computer based feature selection for diagnosing coronary artery disease

Prabowo, Dwi Wahyu; Setiawan, Noor Akhmad; Nugroho, Herminarto

doi:10.2495/intelsys130221

Cited by 6 publications

(16 citation statements)

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“…When compared with the proposed system, Akrami et al [ 28 ] achieved better results, but the resulting performance was still as good as that of category classification. Similar to the work by Prabowo et al [ 29 ], improved results were obtained for sensitivity and F-measure when the process is done with randomize variable selection for every 10-fold and it is performed 10 times. When compared with the case when no variable selection is carried out, the performance of the propose system is still better in terms of sensitivity and F-measure.…”

Section: Discussionsupporting

confidence: 55%

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

2016

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ObjectivesCoronary 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.MethodsThis 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.ResultsThe 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 F-measure 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%.ConclusionsBased on these results it can be concluded that the proposed system is able to deliver good performance in the category of classification.

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Section: Discussionsupporting

confidence: 55%

“…The t -test results showed that the best performance classification method was SMO. The same approach was also adopted by Prabowo et al [ 29 ]. The research also investigated the performance of the algorithm computational intelligence.…”

Section: Introductionmentioning

confidence: 99%

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

2016

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“…The test results showed that the SMO gives better performance of the other algorithm. Other similar studies have also been conducted by [6]. This study is similar to that done before, by using the binary classification approach.…”

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confidence: 62%

“…It is different for OAA-ECOC-SVM and SVM there is some level of value 0. If we compare with SMO (SVM Optimization) with a binary classification approach made [4] and [6], SVM multiclass have more stable performance in any type or level of coronary heart disease. The downside of multiclass classification is only able to give a good performance in diagnosing healthy, and not good for the other type or level.…”

Section: Discussionmentioning

confidence: 99%

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Performance Analysis of Multiclass Support Vector Machine Classification for Diagnosis of Coronary Heart Diseases

Wiharto¹,

Kusnanto²,

Herianto³

2015

IJCSA

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Automatic diagnosis of coronary heart disease helps the doctor to support in decision making a diagnosis. Coronary heart disease have some types or levels. Referring to the UCI Repository dataset, it divided into 4 types or levels that are labeled numbers 1-4 (low, medium, high and serious). The diagnosis models can be analyzed with multiclass classification approach. One of multiclass classification approach used, one of which is a support vector machine (SVM). The SVM use due to strong performance of SVM in binary classification. This research study multiclass performance classification support vector machine to diagnose the type or level of coronary heart disease. Coronary heart disease patient data taken from the UCI Repository. Stages in this study is preprocessing, which consist of, to normalizing the data, divide the data into data training and testing. The next stage of multiclass classification and performance analysis. This study uses multiclass SVM algorithm, namely: Binary . The experiment results showed that the multiclass SVM classification algorithm with the algorithm BT-SVM, OAA-SVM and the ECOC-SVM,gave the highest Recall in the diagnosis of type or healthy level with a value above 90%, precision 82.143% and 86.793% F-measure,. For all kinds of algorithms, except binary OAA-SVM algorithm gave the highest recall 0.0% for the type or level of sickhigh and sick-serious, and ECOC-SVM algorithm gave the highest recall 0.0% for sick-medium and sickserious. While the type or level other, the performance of recall, precision and F-measure between 20% -30%,. The conclusion that can be drawn is that the approach to the multiclass classification algorithm BT-SVM, OAO-SVM, DDAG-SVM to diagnosis the type or level of coronary heart disease provides better performance, than the binary classification approach.

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Strategic value of Online Social Networks (OSNs) in supply chain networks during COVID-19

Haque

Islam

Deb

et al. 2022

Cogent Business & Management

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A study of data randomization on a computer based feature selection for diagnosing coronary artery disease

Cited by 6 publications

References 10 publications

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

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

Performance Analysis of Multiclass Support Vector Machine Classification for Diagnosis of Coronary Heart Diseases

Strategic value of Online Social Networks (OSNs) in supply chain networks during COVID-19

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