A heart failure diagnosis model based on support vector machine

Yang, Guiqiu; Ren, Yinzi; Pan, Qing; Ning, Gangmin; Gong, Shijin; Cai, Guolong; Zhang, Zhaocai; Li, Li; Yan, Jing

doi:10.1109/bmei.2010.5639619

Cited by 16 publications

(6 citation statements)

References 15 publications

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“…Yang et al [24] proposed a classifier based on support-vector machine, which achieved an accuracy of 74% (tenfold cross-validation estimate) in discriminating between mild CHF (NYHA I) and moderate/severe CHF patients (NYHA II and III). We underline that the classifier proposed by Guidi [23] was based on anamnestic and instrumental data (not including HRV measures), and the one by Yang et al [24] was based on 12 parameters, including LF/HF and other parameters from clinical tests (blood test, echocardiography test, electrocardiography test, chest radiography test, and 6-min walk distance test). For that reason, some parameters needed by the automatic classifier proposed by Guidi [23] or Yang et al [24] should be entered by physicians, while the adoption of only HRV measures, as in the current study, enables a completely automatic assessment.…”

Section: Discussionmentioning

confidence: 99%

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Classification Tree for Risk Assessment in Patients Suffering From Congestive Heart Failure via Long-Term Heart Rate Variability

Melillo

Luca

Bracale

et al. 2013

IEEE J. Biomed. Health Inform.

163

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This study aims to develop an automatic classifier for risk assessment in patients suffering from congestive heart failure (CHF). The proposed classifier separates lower risk patients from higher risk ones, using standard long-term heart rate variability (HRV) measures. Patients are labeled as lower or higher risk according to the New York Heart Association classification (NYHA). A retrospective analysis on two public Holter databases was performed, analyzing the data of 12 patients suffering from mild CHF (NYHA I and II), labeled as lower risk, and 32 suffering from severe CHF (NYHA III and IV), labeled as higher risk. Only patients with a fraction of total heartbeats intervals (RR) classified as normal-to-normal (NN) intervals (NN/RR) higher than 80% were selected as eligible in order to have a satisfactory signal quality. Classification and regression tree (CART) was employed to develop the classifiers. A total of 30 higher risk and 11 lower risk patients were included in the analysis. The proposed classification trees achieved a sensitivity and a specificity rate of 93.3% and 63.6%, respectively, in identifying higher risk patients. Finally, the rules obtained by CART are comprehensible and consistent with the consensus showed by previous studies that depressed HRV is a useful tool for risk assessment in patients suffering from CHF.

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Section: Discussionmentioning

confidence: 99%

“…However, to the best of the authors' knowledge, these classifiers are not based on HRV features, except for those proposed by Yang et al [24] who included HRV features but did not provide details about the related processing.…”

Section: Introductionmentioning

confidence: 99%

Classification Tree for Risk Assessment in Patients Suffering From Congestive Heart Failure via Long-Term Heart Rate Variability

Melillo

Luca

Bracale

et al. 2013

IEEE J. Biomed. Health Inform.

163

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“…The accuracy of their HF diagnosis was 74.44% overall, which was a significant increase over earlier studies which they matched their findings. Accuracy reaches 87.5%, especially in the HF-prone group, which suggests that the suggested methodology is workable for HF early detection ( 40 ). Although the AHF classification performance was partially good in most of these studies, XAI was not examined with model calibration.…”

Section: Discussionmentioning

confidence: 93%

Analysis of hematological indicators via explainable artificial intelligence in the diagnosis of acute heart failure: a retrospective study

Yilmaz,

Yagin,

Colak

et al. 2024

Front. Med.

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IntroductionAcute heart failure (AHF) is a serious medical problem that necessitates hospitalization and often results in death. Patients hospitalized in the emergency department (ED) should therefore receive an immediate diagnosis and treatment. Unfortunately, there is not yet a fast and accurate laboratory test for identifying AHF. The purpose of this research is to apply the principles of explainable artificial intelligence (XAI) to the analysis of hematological indicators for the diagnosis of AHF.MethodsIn this retrospective analysis, 425 patients with AHF and 430 healthy individuals served as assessments. Patients’ demographic and hematological information was analyzed to diagnose AHF. Important risk variables for AHF diagnosis were identified using the Least Absolute Shrinkage and Selection Operator (LASSO) feature selection. To test the efficacy of the suggested prediction model, Extreme Gradient Boosting (XGBoost), a 10-fold cross-validation procedure was implemented. The area under the receiver operating characteristic curve (AUC), F1 score, Brier score, Positive Predictive Value (PPV), and Negative Predictive Value (NPV) were all computed to evaluate the model’s efficacy. Permutation-based analysis and SHAP were used to assess the importance and influence of the model’s incorporated risk factors.ResultsWhite blood cell (WBC), monocytes, neutrophils, neutrophil-lymphocyte ratio (NLR), red cell distribution width-standard deviation (RDW-SD), RDW-coefficient of variation (RDW-CV), and platelet distribution width (PDW) values were significantly higher than the healthy group (p < 0.05). On the other hand, erythrocyte, hemoglobin, basophil, lymphocyte, mean platelet volume (MPV), platelet, hematocrit, mean erythrocyte hemoglobin (MCH), and procalcitonin (PCT) values were found to be significantly lower in AHF patients compared to healthy controls (p < 0.05). When XGBoost was used in conjunction with LASSO to diagnose AHF, the resulting model had an AUC of 87.9%, an F1 score of 87.4%, a Brier score of 0.036, and an F1 score of 87.4%. PDW, age, RDW-SD, and PLT were identified as the most crucial risk factors in differentiating AHF.ConclusionThe results of this study showed that XAI combined with ML could successfully diagnose AHF. SHAP descriptions show that advanced age, low platelet count, high RDW-SD, and PDW are the primary hematological parameters for the diagnosis of AHF.

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“…For example, El Omary et al [8] employed serveral CNNs for the purpose of detecting cardiac arrhythmia based on electrocardiogram (ECG) two-dimensional (2D) images; in addition, they [9] utilized a variety of pre-trained CNN models to diagnose heart failure in Radiograph images. Next, Yang et al [10] introduced a model aiming at early heart failure diagnosis using a combination of Bayesian principal component analysis (BPCA) and support vector machine (SVM) resulting in an accuracy rate of 74.4%. Afterwards, Miao et al [11] employed DL to devise a system that enhances he dependability and efficiency of CVDs diagnosis.…”

Section: Related Workmentioning

confidence: 99%

Attention mechanism-based model for cardiomegaly recognition in chest X-Ray images

El Omary,

Lahrache,

El Ouazzani

2024

IJ-AI

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Recently, cardiovascular diseases (CVDs) have become a rapidly growing problem in the world, especially in developing countries. The latter are facing a lifestyle change that introduces new risk factors for heart disease, that requires a particular and urgent interest. Besides, cardiomegaly is a sign of cardiovascular diseases that refers to various conditions; it is associated with the heart enlargement that can be either transient or permanent depending on certain conditions.Furthermore, cardiomegaly is visible on any imaging test including Chest X-Radiation (X-Ray) images; which are one of the most common tools used by Cardiologists to detect and diagnose many diseases. In this paper, we propose an innovative deep learning (DL) model based on an attention module and MobileNet architecture to recognize Cardiomegaly patients using the popular Chest X-Ray8 dataset. Actually, the attention module captures the spatial relationship between the relevant regions in Chest X-Ray images. The experimental results show that the proposed model achieved interesting results with an accuracy rate of 81% which makes it suitable for detecting cardiomegaly disease.

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A heart failure diagnosis model based on support vector machine

Cited by 16 publications

References 15 publications

Classification Tree for Risk Assessment in Patients Suffering From Congestive Heart Failure via Long-Term Heart Rate Variability

Classification Tree for Risk Assessment in Patients Suffering From Congestive Heart Failure via Long-Term Heart Rate Variability

Analysis of hematological indicators via explainable artificial intelligence in the diagnosis of acute heart failure: a retrospective study

Attention mechanism-based model for cardiomegaly recognition in chest X-Ray images

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