Accurate diagnosis is required before performing proper treatments for coronary heart disease. Machine learning based approaches have been proposed by many researchers to improve the accuracy of coronary heart disease diagnosis. Ensemble learning and cascade generalization are among the methods which can be used to improve the generalization ability of learning algorithm. The objective of this study is to develop heart disease diagnosis method based on ensemble learning and cascade generalization. Cascade generalization method with loose coupling strategy is proposed in this study. C4. 5 and RIPPER algorithm were used as meta-level algorithm and Naive Bayes was used as baselevel algorithm. Bagging and Random Subspace were evaluated for constructing the ensemble. The hybrid cascade ensemble methods are compared with the learning algorithms in non-ensemble mode and non-cascade mode. The methods are also compared with Rotation Forest. Based on the evaluation result, the hybrid cascade ensemble method demonstrated the best result for the given heart disease diagnosis case. Accuracy and diversity evaluation was performed to analyze the impact of the cascade strategy. Based on the result, the accuracy of the classifiers in the ensemble is increased but the diversity is decreased.
Coronary heart disease, which is a form of cardiovascular disease (CVD), is the leading cause of death worldwide. The odds of survival are good if it is found or diagnosed early. The current report discusses a comparative approach to the classification of coronary heart disease datasets using machine learning (ML) algorithms. The current study created and tested several machine-learning-based classification models. The dataset was subjected to Smote to handle unbalanced classes and feature selection technique in order to assess the impact on two distinct performance metrics. The results show t…
Sebelas Maret University（塞贝拉斯·马雷特大学）；Gadjah Mada University（加查马达大学）
Automatic diagnosis of coronary heart disease helps the doctor to support in decision making a diagnosis. Coronary heart disease have some types or levels. Referring to the UCI Repository dataset, it divided into 4 types or levels that are labeled numbers 1-4 (low, medium, high and serious). The diagnosis models can be analyzed with multiclass classification approach. One of multiclass classification approach used, one of which is a support vector machine (SVM). The SVM use due to strong performance of SVM in binary classification. This research study multiclass performance classification supp…
Every year, thousands of innocent people die due to heart attacks. Often undiagnosed heart attacks can hit people by surprise since many current medical plans don't cover the costs to require the searching of calcification on these scans. Only if someone is suspected to have a heart problem, a gated CT scan is taken, otherwise, there's no way for the patient to be aware of a possible heart attack/disease. While nongated CT scans are more periodically taken, it is harder to detect calcification and is usually taken for a purpose other than locating calcification in arteries. In fact, in real ti…
University of Georgia（佐治亚大学）；Beijing Hospital（北京医院）
Background: Extensive clinical evidence suggests that a preventive screening of coronary heart disease (CHD) at an earlier stage can greatly reduce the mortality rate. We use 64 two-dimensional speckle tracking echocardiography (2D-STE) features and seven clinical features to predict whether one has CHD. Methods: We develop a machine learning approach that integrates a number of popular classification methods together by model stacking, and generalize the traditional stacking method to a two-step stacking method to improve the diagnostic performance. Results: By borrowing strengths from multip…
INESC-ID（里斯本系统与计算机工程研究所）；Instituto Superior Técnico, University of Lisbon（里斯本大学高等技术学院）；Lisbon School of Medicine, University of Lisbon（里斯本大学里斯本医学院）
Coronary X-ray angiography is a crucial clinical procedure for the diagnosis and treatment of coronary artery disease, which accounts for roughly 16% of global deaths every year. However, the images acquired in these procedures have low resolution and poor contrast, making lesion detection and assessment challenging. Accurate coronary artery segmentation not only helps mitigate these problems, but also allows the extraction of relevant anatomical features for further analysis by quantitative methods. Although automated segmentation of coronary arteries has been proposed before, previous approa…
Recent years have seen growing interest in the diagnosis of Coronary Artery Disease (CAD) with machine learning methods to reduce the cost and health implications of conventional diagnosis. This paper introduces a CAD diagnosis method with a novel feature extraction technique called the Profile-Based Binary Feature Extraction (PBBFE). In this method, after partitioning numerical features, frequent itemsets are extracted by the Apriori algorithm and then used as features to increase the CAD diagnosis accuracy. The proposed method consists of two main phases. In the first phase, each patient is …
Feature Selection (FS) has become the focus of much research on decision support systems areas for which data sets with tremendous number of variables are analyzed. In this paper we present a new method for the diagnosis of Coronary Artery Diseases (CAD) founded on Genetic Algorithm (GA) wrapped Bayes Naive (BN) based FS. Basically, CAD dataset contains two classes defined with 13 features. In GA BN algorithm, GA generates in each iteration a subset of attributes that will be evaluated using the BN in the second step of the selection procedure. The final set of attribute contains the most rele…
Cross-Task Data Augmentation by Pseudo-label Generation for Region Based Coronary Artery Instance Segmentation
Nepal Applied Mathematics and Informatics Institute for research(NAAMII)（尼泊尔应用数学与信息学研究所）；Fogsphere(Redev AI Ltd.)（Fogsphere（Redev AI有限公司））；University of Lausanne（洛桑大学）；School of Natural and Computing Sciences, University of Aberdeen（阿伯丁大学自然与计算科学学院）
University of Birjand（比尔詹德大学）；Maragheh Faculty of Medical Sciences（马拉盖医科大学）；Queensland University of Technology（昆士兰科技大学）；Bauhaus Universität-Weimar（魏玛包豪斯大学）；Obuda University（奥布达大学）；Duy Tan University（越南大智大学）；Ton Duc Thang University（东德大学）
Heart disease is one of the most common diseases in middle-aged citizens. Among the vast number of heart diseases, the coronary artery disease (CAD) is considered as a common cardiovascular disease with a high death rate. The most popular tool for diagnosing CAD is the use of medical imaging, e.g., angiography. However, angiography is known for being costly and also associated with a number of side effects. Hence, the purpose of this study is to increase the accuracy of coronary heart disease diagnosis through selecting significant predictive features in order of their ranking. In this study, …
Vellore Institute of Technology（韦洛尔理工学院）；Centre for Development of Advanced Computing（高级计算发展中心）
The application of data mining, machine learning and artificial intelligence techniques in the field of diagnostics is not a new concept, and these techniques have been very successfully applied in a variety of applications, especially in dermatology and cancer research. But, in the case of medical problems that involve tests resulting in true or false (binary classification), the data generally has a class imbalance with samples majorly belonging to one class (ex: a patient undergoes a regular test and the results are false). Such disparity in data causes problems when trying to model predict…
A machine learning approach for Premature Coronary Artery Disease Diagnosis according to Different Ethnicities in Iran
Fasa University（法萨大学）；Deakin University（迪肯大学）；Queensland University of Technology（昆士兰科技大学）；Isfahan University of Medical Sciences（伊斯法罕医科大学）；University of Tabriz（大不里士大学）；Iran University of Medical Sciences（伊朗医科大学）；University of British Columbia（不列颠哥伦比亚大学）
Queen’s University（女王大学）；University of California, Berkeley（加州大学伯克利分校）；University of Virginia（弗吉尼亚大学）；University of Pittsburgh（匹兹堡大学）
This study proposes an efficient neural network with convolutional layers to classify significantly class-imbalanced clinical data. The data are curated from the National Health and Nutritional Examination Survey (NHANES) with the goal of predicting the occurrence of Coronary Heart Disease (CHD). While the majority of the existing machine learning models that have been used on this class of data are vulnerable to class imbalance even after the adjustment of class-specific weights, our simple two-layer CNN exhibits resilience to the imbalance with fair harmony in class-specific performance. In …
ImageCAS: A Large-Scale Dataset and Benchmark for Coronary Artery Segmentation based on Computed Tomography Angiography Images
Guangdong University of Technology（广东工业大学）；Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences)（广东省人民医院（广东省医学科学院））；Southern Medical University（南方医科大学）；Missouri University of Science and Technology（密苏里科技大学）
RCE；East Atlanta Cardiology（东亚特兰大心脏病学中心）；Northside Hospital Gwinnett（格温内特北区医院）
Coronary artery disease(CAD) is the most common type of heart disease and the leading cause of death worldwide[1]. A progressive state of this disease marked by plaque rupture and clot formation in the coronary arteries, also known as an acute coronary syndrome (ACS), is a condition of the heart associated with sudden, reduced blood flow caused due to partial or full occlusion of coronary vasculature that normally perfuses the myocardium and nerve bundles, compromising the proper functioning of the heart. Often manifesting with pain or tightness in the chest as the second most common cause of …
Background and purpose: Heart disease has been one of the most important causes of death in the last 10 years, so the use of classification methods to diagnose and predict heart disease is very important. If this disease is predicted before menstruation, it is possible to prevent high mortality of the disease and provide more accurate and efficient treatment methods. Materials and Methods: Due to the selection of input features, the use of basic algorithms can be very time-consuming. Reducing dimensions or choosing a good subset of features, without risking accuracy, has great importance for b…
Cardiovascular disease (CD) is the number one leading cause of death worldwide, accounting for more than 17 million deaths in 2015. Critical indicators of CD include heart murmurs, intense sounds emitted by the heart during periods of irregular blood flow. Current diagnosis of heart murmurs relies on echocardiography (ECHO), which costs thousands of dollars and medical professionals to analyze the results, making it very unsuitable for areas with inadequate medical facilities. Thus, there is a need for an accessible alternative. Based on a simple interface and deep learning, HeartFit allows us…
Applied Science Private University（应用科学私立大学）；University of Sharjah（沙迦大学）
Coronary Artery Disease (CAD) is one of the leading causes of death worldwide, and so it is very important to correctly diagnose patients with the disease. For medical diagnosis, machine learning is a useful tool, however features and algorithms must be carefully selected to get accurate classification. To this effect, three feature selection methods have been used on 13 input features from the Cleveland dataset with 297 entries, and 7 were selected. The selected features were used to train three different classifiers, which are SVM, Naïve Bayes and KNN using 10-fold cross-validation. The resu…
University of New South Wales（新南威尔士大学）；Harvard University（哈佛大学）
Electrocardiography (ECG) signal is a highly applied measurement for individual heart condition, and much effort have been endeavored towards automatic heart arrhythmia diagnosis based on machine learning. However, traditional machine learning models require large investment of time and effort for raw data preprocessing and feature extraction, as well as challenged by poor classification performance. Here, we propose a novel deep learning model, named Attention-Based Convolutional Neural Networks (ABCNN) that taking advantage of CNN and multi-head attention, to directly work on the raw ECG sig…
Heart disease is one of the significant challenges in today's world and one of the leading causes of many deaths worldwide. Recent advancement of machine learning (ML) application demonstrates that using electrocardiogram (ECG) and patient data, detecting heart disease during the early stage is feasible. However, both ECG and patient data are often imbalanced, which ultimately raises a challenge for the traditional ML to perform unbiasedly. Over the years, several data level and algorithm level solutions have been exposed by many researchers and practitioners. To provide a broader view of the …
James B. Conant High School（詹姆斯·B·柯南特高中）；Cleveland Clinic Foundation（克利夫兰诊所基金会）
Heart disease is the leading cause of death worldwide. Currently, 33% of cases are misdiagnosed, and approximately half of myocardial infarctions occur in people who are not predicted to be at risk. The use of Artificial Intelligence could reduce the chance of error, leading to possible earlier diagnoses, which could be the difference between life and death for some. The objective of this project was to develop an application for assisted heart disease diagnosis using Machine Learning (ML) and Deep Neural Network (DNN) algorithms. The dataset was provided from the Cleveland Clinic Foundation, …
Utilizing AI Language Models to Identify Prognostic Factors for Coronary Artery Disease: A Study in Mashhad Residents
University of Turin（都灵大学）；Golestan University of Medical Sciences（戈勒斯坦医科大学）；Macquarie University（麦考瑞大学）；Mashhad University of Medical Sciences（马什哈德医科大学）
Machine learning prediction of obstructive coronary artery disease using opportunistic coronary calcium and epicardial fat assessments from CT calcium scoring scans
Department of Biomedical Engineering, Virginia Commonwealth University（弗吉尼亚联邦大学生物医学工程系）；Department of Biomedical Engineering, Case Western Reserve University（凯斯西储大学生物医学工程系）；Harrington Heart and Vascular Institute, University Hospitals Cleveland Medical Center（克利夫兰医学中心哈灵顿心脏和血管研究所）；BHF Centre for Cardiovascular Science, University of Edinburgh（爱丁堡大学BHF心血管科学中心）；Department of Radiology, Case Western Reserve University（凯斯西储大学放射学系）
LASTIMI, High School of Technology Mohammed V University in Rabat Sale, Morocco；LASTIMI, Mohammedia School of Engineers Mohammed V University in Rabat Rabat, Morocco；CISIEV. Faculty of Science；Technology in Marrakech Cadi Ayyad University Marrakech, Morocco
Coronary artery disease (CAD) is one of the most common cardiac diseases worldwide and causes disability and economic burden. It is the world's leading and most serious cause of mortality, with approximately 80% of deaths reported in low- and middle-income countries. The preferred and most precise diagnostic tool for CAD is angiography, but it is invasive, expensive, and technically demanding. However, the research community is increasingly interested in the computer-aided diagnosis of CAD via the utilization of machine learning (ML) methods. The purpose of this work is to present an e-diagnos…
The diagnosis of heart diseases is a difficult task generally addressed by an appropriate examination of patients clinical data. Recently, the use of heart rate variability (HRV) analysis as well as of some machine learning algorithms, has proved to be a valuable support in the diagnosis process. However, till now, ischemic heart disease (IHD) has been diagnosed on the basis of Artificial Neural Networks (ANN) applied only to signs, symptoms and sequential ECG and coronary angiography, an invasive tool, while could be probably identified in a non-invasive way by using parameters extracted from…
Austrian Research Institute for Artificial Intelligence（奥地利人工智能研究院）；University of Manchester（曼彻斯特大学）
Despite of the pain and limited accuracy of blood tests for early recognition of cardiovascular disease, they dominate risk screening and triage. On the other hand, heart rate variability is non-invasive and cheap, but not considered accurate enough for clinical practice. Here, we tackle heart beat interval based classification with deep learning. We introduce an end to end differentiable hybrid architecture, consisting of a layer of biological neuron models of cardiac dynamics (modified FitzHugh Nagumo neurons) and several layers of a standard feed-forward neural network. The proposed model i…
College of Computing and Information Technology, University of Bisha（比沙大学计算与信息技术学院）
Nowadays, heart disease is the leading cause of death worldwide. Predicting heart disease is a complex task since it requires experience along with advanced knowledge. Internet of Things (IoT) technology has lately been adopted in healthcare systems to collect sensor values for heart disease diagnosis and prediction. Many researchers have focused on the diagnosis of heart disease, yet the accuracy of the diagnosis results is low. To address this issue, an IoT framework is proposed to evaluate heart disease more accurately using a Modified Deep Convolutional Neural Network (MDCNN). The smartwat…
Improving AI-Based Canine Heart Disease Diagnosis with Expert-Consensus Auscultation Labeling
Fraunhofer Institute for Software and Systems Engineering ISST（弗劳恩霍夫软件与系统工程研究所）；Department of Computer Science, TU Dortmund University（图林根大学计算机科学系）；Department of Clinical Sciences, Swedish University of Agricultural Sciences（瑞典农业科学大学临床科学系）；Clinic for Small Animal Medicine, Ludwig Maximilian University of Munich（慕尼黑路德维希-马克西米利安大学小动物医学诊所）；Boehringer Ingelheim Vetmedica GmbH（波士顿-英氏兽医医药有限公司）；Department of Health Informatics, School of Medicine, Witten/Herdecke University（乌尔姆-赫尔德克大学医学院健康信息学系）
From a fresh data science perspective, this thesis discusses the prediction of coronary artery disease based on genetic variations at the DNA base pair level, called Single-Nucleotide Polymorphisms (SNPs), collected from the Ontario Heart Genomics Study (OHGS). First, the thesis explains two commonly used supervised learning algorithms, the k-Nearest Neighbour (k-NN) and Random Forest classifiers, and includes a complete proof that the k-NN classifier is universally consistent in any finite dimensional normed vector space. Second, the thesis introduces two dimensionality reduction steps, Rando…
Massachusetts Institute of Technology（麻省理工学院）；Massachusetts General Hospital（麻省总医院）
Current clinical practice guidelines for managing Coronary Artery Disease (CAD) account for general cardiovascular risk factors. However, they do not present a framework that considers personalized patient-specific characteristics. Using the electronic health records of 21,460 patients, we created data-driven models for personalized CAD management that significantly improve health outcomes relative to the standard of care. We develop binary classifiers to detect whether a patient will experience an adverse event due to CAD within a 10-year time frame. Combining the patients' medical history an…
The University of Tennessee（田纳西大学）；University of Oklahoma Health Sciences Center（俄克拉荷马大学健康科学中心）
Cardiac disease is the leading cause of death in the US. Accurate heart disease detection is of critical importance for timely medical treatment to save patients' lives. Routine use of electrocardiogram (ECG) is the most common method for physicians to assess the electrical activities of the heart and detect possible abnormal cardiac conditions. Fully utilizing the ECG data for reliable heart disease detection depends on developing effective analytical models. In this paper, we propose a two-level hierarchical deep learning framework with Generative Adversarial Network (GAN) for automatic diag…
Machine Learning-Based Model for Postoperative Stroke Prediction in Coronary Artery Disease
University of Southern California（南加州大学）；Iran University of Medical Sciences（伊朗医科大学）；California State University, Long Beach (CSULB)（加州州立大学长滩分校）；California State Polytechnic University, Pomona（加州州立理工大学波莫纳分校）
Heart disease is the leading cause of death, and experts estimate that approximately half of all heart attacks and strokes occur in people who have not been flagged as "at risk." Thus, there is an urgent need to improve the accuracy of heart disease diagnosis. To this end, we investigate the potential of using data analysis, and in particular the design and use of deep neural networks (DNNs) for detecting heart disease based on routine clinical data. Our main contribution is the design, evaluation, and optimization of DNN architectures of increasing depth for heart disease diagnosis. This work…
Personalized Heart Disease Detection via ECG Digital Twin Generation
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Guangdong Provincial People’s Hospital, Southern Medical University（南方医科大学广东省人民医院）；Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences（广东省人民医院广东省心血管病研究所、广东省医学科学院）；School of Public Health, Zhejiang University（浙江大学公共卫生学院）
