A Hyper-Solution Framework for SVM Classification: Application for Predicting Destabilizations in Chronic Heart Failure Patients

Candelieri, Antonio; Conforti, Domenico

doi:10.2174/1874431101004010136

Cited by 15 publications

(8 citation statements)

References 18 publications

(15 reference statements)

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“…Similar to our study, incorporation of clinical and exercise data significantly improved the predictive accuracy of the network compared to a network based on image data alone (P<.05). More recent ML algorithms have been applied in cardiovascular medicine to predict increased risk of decompensated heart failure 41 and onset of atrial fibrillation. 42 These techniques have also been used in gene mapping and cellular biology.…”

Section: Discussionmentioning

confidence: 99%

Improved accuracy of myocardial perfusion SPECT for detection of coronary artery disease by machine learning in a large population

Arsanjani

Dey

et al. 2013

Journal of Nuclear Cardiology

122

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Objective We aimed to improve the diagnostic accuracy of myocardial perfusion SPECT (MPS) by integrating clinical data and quantitative image features with machine learning (ML) algorithms. Methods 1,181 rest 201Tl/stress 99mTc-sestamibi dual-isotope MPS studies [713 consecutive cases with correlating invasive coronary angiography (ICA) and suspected coronary artery disease (CAD) and 468 with low likelihood (LLk) of CAD <5%] were considered. Cases with stenosis <70% by ICA and LLk of CAD were considered normal. Total stress perfusion deficit (TPD) for supine/prone data, stress/rest perfusion change, and transient ischemic dilatation were derived by automated perfusion quantification software and were combined with age, sex, and post-electrocardiogram CAD probability by a boosted ensemble ML algorithm (Logit-Boost). The diagnostic accuracy of the model for prediction of obstructive CAD ≥70% was compared to standard prone/supine quantification and to visual analysis by two experienced readers utilizing all imaging, quantitative, and clinical data. Tenfold stratified cross-validation was performed. Results The diagnostic accuracy of ML (87.3% ± 2.1%) was similar to Expert 1 (86.0% ± 2.1%), but superior to combined supine/prone TPD (82.8% ± 2.2%) and Expert 2 (82.1% ± 2.2%) (P <.01). The receiver operator characteristic areas under curve for ML algorithm (0.94 ± 0.01) were higher than those for TPD and both visual readers (P <.001). The sensitivity of ML algorithm (78.9% ± 4.2%) was similar to TPD (75.6% ± 4.4%) and Expert 1 (76.3% ± 4.3%), but higher than that of Expert 2 (71.1% ± 4.6%), (P <.01). The specificity of ML algorithm (92.1% ± 2.2%) was similar to Expert 1 (91.4% ± 2.2%) and Expert 2 (88.3% ± 2.5%), but higher than TPD (86.8% ± 2.6%), (P <.01). Conclusion ML significantly improves diagnostic performance of MPS by computational integration of quantitative perfusion and clinical data to the level rivaling expert analysis.

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Section: Discussionmentioning

confidence: 99%

Improved accuracy of myocardial perfusion SPECT for detection of coronary artery disease by machine learning in a large population

Arsanjani

Dey

et al. 2013

Journal of Nuclear Cardiology

122

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“…The value of evaluation measures is 97.37% accuracy, 100.00% sensitivity, and 2.78% False Positive Rate. Based on this observation they further extended their research activity, by proposing the SVM hyper-solution framework [54]. The term “hyper-solution” is used to describe SVM based on meta-heuristics (Tabu-Search and Genetic Algorithm) searching for the most reliable hyper-classifier (SVM with a basic kernel, SVM with a combination of kernel, and ensemble of SVMs), and for its optimal configuration.…”

Section: Prediction Of Adverse Eventsmentioning

confidence: 99%

Heart Failure: Diagnosis, Severity Estimation and Prediction of Adverse Events Through Machine Learning Techniques

Tripoliti

Papadopoulos

Karanasiou

et al. 2017

Computational and Structural Biotechnology Journal

162

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Heart failure is a serious condition with high prevalence (about 2% in the adult population in developed countries, and more than 8% in patients older than 75 years). About 3–5% of hospital admissions are linked with heart failure incidents. Heart failure is the first cause of admission by healthcare professionals in their clinical practice. The costs are very high, reaching up to 2% of the total health costs in the developed countries. Building an effective disease management strategy requires analysis of large amount of data, early detection of the disease, assessment of the severity and early prediction of adverse events. This will inhibit the progression of the disease, will improve the quality of life of the patients and will reduce the associated medical costs. Toward this direction machine learning techniques have been employed. The aim of this paper is to present the state-of-the-art of the machine learning methodologies applied for the assessment of heart failure. More specifically, models predicting the presence, estimating the subtype, assessing the severity of heart failure and predicting the presence of adverse events, such as destabilizations, re-hospitalizations, and mortality are presented. According to the authors' knowledge, it is the first time that such a comprehensive review, focusing on all aspects of the management of heart failure, is presented.

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“…Statistical learning algorithms have been used in cardiovascular medicine to predict multiple features including those at increased risk of decompensated heart failure (29) as well as predictors of onset of atrial fibrillation (30). Prior studies have demonstrated that quantitative analysis can be a useful supplement to the visual analysis (31,32), providing an accurate and objective method for assessment of the extent, severity, and reversibility of perfusion defects.…”

Section: Discussionmentioning

confidence: 99%

Improved Accuracy of Myocardial Perfusion SPECT for the Detection of Coronary Artery Disease Using a Support Vector Machine Algorithm

Arsanjani

Dey

et al. 2013

J Nucl Med

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We aimed to improve the diagnostic accuracy of automatic myocardial perfusion SPECT (MPS) interpretation analysis for the prediction of coronary artery disease (CAD) by integrating several quantitative perfusion and functional variables for noncorrected (NC) data by Support Vector Machine (SVM) algorithm, a computer method for machine learning. Methods: Rest-stress gated 99m Tc MPS NC studies (n 5 957) from 623 consecutive patients with correlating invasive coronary angiography and 334 with a low likelihood of CAD (,5%) were assessed. Stenosis $50% in left main or $70% in all other vessels was considered abnormal. Total perfusion deficit (TPD) was computed automatically. In addition, ischemic changes (ISCHs) and ejection fraction changes (EFCs) between stress and rest were derived by quantitative software. The SVM was trained using a group of 125 patients (25 with low-likelihood, 25 with 0-vessel, 25 with 1-vessel, 25 with 2-vessel, and 25 with 3-vessel CAD) with the above quantitative variables and second-order polynomial fitting. The remaining patients (n 5 832) were categorized using probability estimates, with CAD defined as a probability estimate $ 0.50. The diagnostic accuracy of SVM was also compared with visual segmental scoring by 2 experienced readers. Results: The sensitivity of SVM (84%) was significantly better than ISCH (75%, P , 0.05) and EFC (31%, P , 0.05). The specificity of SVM (88%) was significantly better than TPD (78%, P , 0.05) and EFC (77%, P , 0.05). The diagnostic accuracy of SVM (86%) was significantly better than TPD (81%), ISCH (81%), or EFC (46%) (P , 0.05 for all). The receiver-operating-characteristic (ROC) area under the curve for SVM (0.92) was significantly better than TPD (0.90), ISCH (0.87), and EFC (0.64) (P , 0.001 for all). The diagnostic accuracy of SVM was comparable to the overall accuracy of both visual readers (86% vs. 84%, P 5 NS). The ROC area under the curve for SVM (0.92) was significantly better than that of both visual readers (0.87 and 0.88, P , 0.03). Conclusion: Computational integration of quantitative perfusion and functional variables using the SVM approach significantly improves the diagnostic accuracy of MPS and can significantly outperform visual assessment based on ROC analysis.

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A Hyper-Solution Framework for SVM Classification: Application for Predicting Destabilizations in Chronic Heart Failure Patients

Cited by 15 publications

References 18 publications

Improved accuracy of myocardial perfusion SPECT for detection of coronary artery disease by machine learning in a large population

Improved accuracy of myocardial perfusion SPECT for detection of coronary artery disease by machine learning in a large population

Heart Failure: Diagnosis, Severity Estimation and Prediction of Adverse Events Through Machine Learning Techniques

Improved Accuracy of Myocardial Perfusion SPECT for the Detection of Coronary Artery Disease Using a Support Vector Machine Algorithm

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