Assessment of exercise stress testing with artificial neural network in determining coronary artery disease and predicting lesion localization

Babaoğlu, İsmail; Baykan, Ömer Kaan; Aygül, Nazif; Özdemi̇r, Kurtuluş; Bayrak, Mehmet

doi:10.1016/j.eswa.2007.11.013

Cited by 35 publications

(18 citation statements)

References 14 publications

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“…Standard datasets for each heart disease are prepared including certain features each with a predefined range of values. Such datasets have been used for knowledge discovery of heart problems in several research studies [4]- [11], [15], [16], [18]- [20].…”

Section: Data Mining Techniques For Heart Disease Diagnosis and Pmentioning

confidence: 99%

“…As mentioned in Section II, the most common type of heart diseases is CAD (Coronary Arteries Disease). For diagnosing CAD, different classification algorithms have been implemented [9]- [20]. In these studies, three features of vessel's stenosis have been employed namely Left Anterior Descending (LAD), Left Circumflex (LCX) and Right Coronary Artery (RCA).…”

Section: Data Mining Techniques For Heart Disease Diagnosis and Pmentioning

confidence: 99%

“…In [20], neural networks algorithm was used for prediction of stenosis of each vessel separately. Ten-fold cross-validation methods were involved to measure the accuracy.…”

Section: Data Mining Techniques For Heart Disease Diagnosis and Pmentioning

confidence: 99%

“…Another study [9] applied Naï ve Bayes, C4.5, and KNN classification algorithms using more sophisticated features that have not been applied in [20]. The study aimed to diagnose CAD via the stenosis of each LAD, LCX or RCA vessel separately.…”

Section: Data Mining Techniques For Heart Disease Diagnosis and Pmentioning

confidence: 99%

See 3 more Smart Citations

An Overview of Data Mining Techniques Applied for Heart Disease Diagnosis and Prediction

Alzahani¹,

Althopity²,

Alghamdi³

et al. 2015

LNIT

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Data mining techniques have been applied magnificently in many fields including business, science, the Web, cheminformatics, bioinformatics, and on different types of data such as textual, visual, spatial, real-time and sensor data. Medical data is still information rich but knowledge poor. There is a lack of effective analysis tools to discover the hidden relationships and trends in medical data obtained from clinical records. This paper reviews the stateof-the-art research on heart disease diagnosis and prediction. Specifically in this paper, we present an overview of the current research being carried out using the data mining techniques to enhance heart disease diagnosis and prediction including decision trees, Naive Bayes classifiers, K-nearest neighbour classification (KNN), support vector machine (SVM), and artificial neural networks techniques. Results show that SVM and neural networks perform positively high to predict the presence of coronary heart diseases (CHD). Decision trees after features reduction is the best recommended classifier to diagnose cardiovascular disease (CVD). Still the performance of data mining techniques to detect coronary arteries diseases (CAD) is not encouraging (between 60%-75%) and further improvements should be pursued.Index Terms-heart disease, data mining, decision tree, naive bayes, K-nearest neighbor, support vector machine 310

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Section: Data Mining Techniques For Heart Disease Diagnosis and Pmentioning

confidence: 99%

Section: Data Mining Techniques For Heart Disease Diagnosis and Pmentioning

confidence: 99%

“…In [20], neural networks algorithm was used for prediction of stenosis of each vessel separately. Ten-fold cross-validation methods were involved to measure the accuracy.…”

Section: Data Mining Techniques For Heart Disease Diagnosis and Pmentioning

confidence: 99%

Section: Data Mining Techniques For Heart Disease Diagnosis and Pmentioning

confidence: 99%

See 2 more Smart Citations

An Overview of Data Mining Techniques Applied for Heart Disease Diagnosis and Prediction

Alzahani¹,

Althopity²,

Alghamdi³

et al. 2015

LNIT

View full text Add to dashboard Cite

show abstract

“…In recent decades, many experts have tried to make CAD diagnosis using computer-aided techniques, such as neural networks [3], the Bayesian model and decision tree [4], support vector machine [5], and the naive Bayes classifier [6]. The purpose of such researches is to help physicians to make CAD diagnosis by a non-invasive procedure.…”

Section: Introductionmentioning

confidence: 99%

CADICA: Diagnosis of Coronary Artery Disease Using the Imperialist Competitive Algorithm

Mahmoodabadi¹,

Abadeh

2014

Journal of Computing Science and Engineering

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Coronary artery disease (CAD) is currently a prevalent disease from which many people suffer. Early detection and treatment could reduce the risk of heart attack. Currently, the golden standard for the diagnosis of CAD is angiography, which is an invasive procedure. In this article, we propose an algorithm that uses data mining techniques, a fuzzy expert system, and the imperialist competitive algorithm (ICA), to make CAD diagnosis by a non-invasive procedure. The ICA is used to adjust the fuzzy membership functions. The proposed method has been evaluated with the Cleveland and Hungarian datasets. The advantage of this method, compared with others, is the interpretability. The accuracy of the proposed method is 94.92% by 11 rules, and the average length of 4. To compare the colonial competitive algorithm with other metaheuristic algorithms, the proposed method has been implemented with the particle swarm optimization (PSO) algorithm. The results indicate that the colonial competition algorithm is more efficient than the PSO algorithm. Category: Smart and intelligent computing

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Early Detection of Coronary Artery Disease Using PSO-Based Neuroevolution Model

Karimi

Jalali

Vanani

et al. 2022

Studies in Computational Intelligence

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Assessment of exercise stress testing with artificial neural network in determining coronary artery disease and predicting lesion localization

Cited by 35 publications

References 14 publications

An Overview of Data Mining Techniques Applied for Heart Disease Diagnosis and Prediction

An Overview of Data Mining Techniques Applied for Heart Disease Diagnosis and Prediction

CADICA: Diagnosis of Coronary Artery Disease Using the Imperialist Competitive Algorithm

Early Detection of Coronary Artery Disease Using PSO-Based Neuroevolution Model

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