Diagnosis of Acute Coronary Syndrome with a Support Vector Machine

Berikol, Göksu Bozdereli; Yıldız, Oktay; Özcan, İsmail Türkay

doi:10.1007/s10916-016-0432-6

Cited by 80 publications

(38 citation statements)

References 18 publications

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“…In the first experiment, we used the order of variables given by the MIFS-U filter to compare the capability of ν-SVM and feed-forward ANN models to predict the risk of death (as high or low) in patients admitted with ACS. In line with previous studies (Berikol et al, 2016;Kumari & Godara, 2011;Xing et al, 2007), the results indicated that the ν-SVM model is superior. However, the classifier biases did not diverge in terms of variable selection since both classifiers identified the same optimal subset of input variables: Age, Any Previous Revascularization, and Creatinine.…”

Section: Resultssupporting

confidence: 91%

A Comparative Study Between Artificial Neural Network and Support Vector Machine for Acute Coronary Syndrome Prognosis

Collazo¹,

Pessôa²,

Bahiense

et al. 2016

Pesqui. Oper.

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ABSTRACT. Despite medical advances, mortality due to acute coronary syndrome remains high. For this reason, it is important to identify the most critical factors for predicting the risk of death in patients hospitalized with this disease. To improve medical decisions, it is also helpful to construct models that enable us to represent how the main driving factors relate to patient outcomes. In this study, we compare the capability of Artificial Neural Network (ANN) and Support Vector Machine (SVM) models to distinguish between patients hospitalized with acute coronary syndrome who have low or high risk of death. Input variables are selected using the wrapper approach associated with a mutual information filter and two new proposed filters based on Euclidean distance. Because of missing data, the use of a filter is an important step in increasing the size of the usable data set and maximizing the performance of the classification models. The computational results indicate that the SVM model performs better. The most relevant input variables are age, any previous revascularization, and creatinine, regardless of the classification algorithms and filters used. However, the Euclidean filters also identify a second important group of input variables: age, creatinine and systemic arterial hypertension.

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Section: Resultssupporting

confidence: 91%

A Comparative Study Between Artificial Neural Network and Support Vector Machine for Acute Coronary Syndrome Prognosis

Collazo¹,

Pessôa²,

Bahiense

et al. 2016

Pesqui. Oper.

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“…Support vector machine (SVM) classifiers are gaining significance as a robust classification tool in cancer genomics (13) and have been used for the diagnosis of various diseases, including chronic kidney disease (14) and acute coronary syndrome (15). In the present study, it was hypothesized that an SVM classifier based on optimal feature genes of PSS could facilitate the diagnosis of the disease.…”

Section: Introductionmentioning

confidence: 99%

A six‑gene support vector machine classifier contributes to the diagnosis of pediatric septic shock

Long

Yang

2020

Mol Med Report

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Septic shock is induced by an uncontrolled inflammatory immune response to pathogens and the survival rate of patients with pediatric septic shock (PSS) is particularly low, with a mortality rate of 25-50%. The present study explored the mechanisms of PSS using four microarray datasets (GSe26378, GSe26440, GSe13904 and GSe4607) that were obtained from the Gene Expression Omnibus database. Based on the MetaDE package, the consistently differentially expressed genes (DEGs) in the four datasets were screened. Using the WGCNA package, the disease-associated modules and genes were identified. Subsequently, the optimal feature genes were further selected using the caret package. Finally, a support vector machine (SVM) classifier based on the optimal feature genes was built using the e1071 package. Initially, there were 2,699 consistent DEGs across the four datasets. From the 10 significantly stable modules across the datasets, four stable modules (including the magenta, purple, turquoise and yellow modules), in which the consistent DEGs were significantly enriched (P<0.05), were further screened. Subsequently, six optimal feature genes (including cysteine rich transmembrane module containing 1, S100 calcium binding protein A9, solute carrier family 2 member 14, stomatin, uridine phosphorylase 1 and utrophin) were selected from the genes in the four stable modules. Additionally, an effective SVM classifier was constructed based on the six optimal genes. The SVM classifier based on the six optimal genes has the potential to be applied for PSS diagnosis. This may improve the accuracy of early PSS diagnosis and suggest possible molecular targets for interventions.

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“…According to the results of this study, the efficiency of these methods is 0.848, 0.818 and 0.810, respectively. In another study, Bricol et al compared the SVM, ANN, Navy Bays and regression methods for predicting ACS (Berikol, Yildiz, and Özcan 2016). According to the results of this study the prediction accuracy of each of these methods is 99.13%, 99.10%, 88.75%, and 91.26%, respectively.…”

Section: Intracoronary Thrombus Detected On Angiography or Autopsymentioning

confidence: 86%

A Web-Based Smart Telecare System for Early Diagnosis of Heart Attack

Hosseinpour

Delavar

Baferani

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Acute coronary syndrome which could lead to a heart attack is a common and dangerous disease in many parts of the world. Telecare can accelerate the diagnosis of the disease to provide user information including his/her location in an emergency to a support unit, to reduce the treatment time. Therefore, it is essential to develop a smart telecare system that monitors the status of the user automatically in real-time. This paper has proposed and implemented a web-based smart telecare system. The architecture of this system is based on an architecture for smart care in a smart city. This system receives information from the user and uses linear regression to detect acute coronary syndrome at the initial stage of the disease. This information is descriptive and selected according to the opinion of the cardiologist. Also, in case of diagnosis of acute coronary syndrome, the user's location is determined and sent to a support unit via email. The accuracy of this system is 77.5%. This accuracy is acceptable compared to the same system that predicts myocardial infarction. As the proposed system, unlike the comparator system, does not use electro cardio graphic signals (ECG), it can significantly reduce the time of diagnosis of acute coronary syndrome and its complications through early diagnosis of the disease and its notification. The innovation of this research is the presentation of an intelligent decision maker system that reports the hearth attack to the decision support system via an email message.

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Diagnosis of Acute Coronary Syndrome with a Support Vector Machine

Cited by 80 publications

References 18 publications

A Comparative Study Between Artificial Neural Network and Support Vector Machine for Acute Coronary Syndrome Prognosis

A Comparative Study Between Artificial Neural Network and Support Vector Machine for Acute Coronary Syndrome Prognosis

A six‑gene support vector machine classifier contributes to the diagnosis of pediatric septic shock

A Web-Based Smart Telecare System for Early Diagnosis of Heart Attack

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