A New ICA-Based Algorithm for Diagnosis of Coronary Artery Disease

Mahmoodabadi, Zahra; Tabrizi, Saeed Shaerbaf

doi:10.1007/978-81-322-2009-1_47

Cited by 2 publications

(3 citation statements)

References 7 publications

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“…Many recent studies address the problem of an early diagnosis of heart disease [27], [30]- [39]. Here, we briefly discuss those which are directly related to our work.…”

Section: Related Workmentioning

confidence: 99%

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NE-nu-SVC: A New Nested Ensemble Clinical Decision Support System for Effective Diagnosis of Coronary Artery Disease

et al. 2019

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Coronary artery disease (CAD) is one of the main causes of cardiac death around the world. Due to its significant impact on the society, early and accurate detection of CAD is essential. This study proposes a novel nested ensemble nu-Support Vector Classification (NE-nu-SVC) model which combines several traditional machine learning methods and ensemble learning techniques for effective diagnosis of CAD. We validated our model using two well-known CAD datasets (Z-Alizadeh Sani and Cleveland). To improve the performance of the model, we selected clinically significant features from the datasets using a genetic search algorithm. To further improve our results, we applied a multi-level filtering technique to balance the data using the ClassBlancer and Resample methods. Our base algorithm, nu-SVC, is performed using four well-known kernel functions (linear, polynomial, radial basis (RBF) and sigmoid). The proposed NE-nu-SVC model provided the highest accuracy of 94.66% and 98.60% to predict CAD entities in the Z-Alizadeh Sani and Cleveland CAD datasets, respectively. Our system can aid the clinicians to diagnose CAD accurately and may probably replace other invasive diagnostic techniques. INDEX TERMS Coronary artery disease (CAD), machine learning, ensemble learning, nested ensemble (NE) model, genetic algorithm.

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“…Many recent studies address the problem of an early diagnosis of heart disease [27], [30]- [39]. Here, we briefly discuss those which are directly related to our work.…”

Section: Related Workmentioning

confidence: 99%

“…Then, the SVM and K-means algorithms were applied. Mahmoodabadi and Tabrizi [39] introduced a new intelligent system, called Imperialist Competitive Algorithm (ICA), to predict CAD. This system included both the decision tree and evolutionary algorithms and provided the average CAD detection accuracy of 94.92%.…”

Section: Related Workmentioning

confidence: 99%

NE-nu-SVC: A New Nested Ensemble Clinical Decision Support System for Effective Diagnosis of Coronary Artery Disease

et al. 2019

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“…Although decision trees have a single output, multiple trees can be utilized to handle various outputs. This technology is commonly utilized in data mining [5].…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of J48 Decision Tree and CART Algorithm for Liver Cancer Symptom Analysis Using Data from Carnegie Mellon University

Chi

2023

Adv. sci. technol. eng. syst. j.

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Liver cancer is a major contributor to cancer-related mortality both in the United States and worldwide. A range of liver diseases, such as chronic liver disease, liver cirrhosis, hepatitis, and liver cancer, play a role in this statistic. Hepatitis, in particular, is the main culprit behind liver cancer. As a consequence, it is decisive to investigate the correlation between hepatitis and symptoms using statistic inspection. In this study, we inspect 155 patient data possessed by CARNEGIE-MELLON UNIVERSITY in 1988 to prognosticate whether an individual died from liver disease using supervised machine learning models for category and connection rules based on 20 different symptom attributes. We compare J48 (Gain Ratio) and CART (Classification and Regression Tree), two decision tree classification algorithms elaborate from ID3 (Iterative Dichotomiser 3), with the Gini index in a Java environment. The data is preprocessed through normalization. Our study demonstrates that J48 outperforms CART, with an average accuracy rate of nearly 87% for the complete specimen, cross-validation, and 66% training data. However, CART has the supreme accurate rate in all samples, with an accuracy rate of 90.3232%. Furthermore, our research indicates that removing the conjunction attribute of the Apriori algorithm does not impact the results. This research showcases the potential for physician and researchers to apply brief machine learning device to attain accurate outcomes and develop treatments based on symptoms.

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A New ICA-Based Algorithm for Diagnosis of Coronary Artery Disease

Cited by 2 publications

References 7 publications

NE-nu-SVC: A New Nested Ensemble Clinical Decision Support System for Effective Diagnosis of Coronary Artery Disease

NE-nu-SVC: A New Nested Ensemble Clinical Decision Support System for Effective Diagnosis of Coronary Artery Disease

Comparative Study of J48 Decision Tree and CART Algorithm for Liver Cancer Symptom Analysis Using Data from Carnegie Mellon University

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