A Clinical Decision Support Platform for the Risk Stratification, Diagnosis, and Prediction of Coronary Artery Disease Evolution (original) (raw)
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Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2017
SMARTool aims to the development of a clinical decision support system (CDSS) for the management and stratification of patients with coronary artery disease (CAD). This will be achieved by performing computational modeling of the main processes of atherosclerotic plaque growth. More specifically, computed tomography coronary angiography (CTCA) is acquired and 3-dimensional (3D) reconstruction is performed for the arterial trees. Then, blood flow and plaque growth modeling is employed simulating the major processes of atherosclerosis, such as the estimation of endothelial shear stress (ESS), the lipids transportation, low density lipoprotein (LDL) oxidation, macrophages migration and plaque development. The plaque growth model integrates information from genetic and biological data of the patients. The SMARTool system enables also the calculation of the virtual functional assessment index (vFAI), an index equivalent to the invasively measured fractional flow reserve (FFR), to provide...
Data mining approach for Coronary Artery Disease screening
Coronary artery disease (CAD) is the major cause of mortality in the world. Although there is a significant level of advancement in medical science and technology, this disease still remains challenging to the common people. The aim of this study is to develop a computer assisted screening system that will help early detection of CAD and improved patient management with the limited resources in the developing countries. The present system is developed from an initial marked data set. Ten risk factors have been investigated for the risk stratification of CAD. Two decision tree models -ID3 and CART, have been applied for finding a preliminary set of rules from the annotated database. The extracted rules have been clinically validated by a group of cardiologists as per their medical experience and acumen in finding a final set of rule base. The dataset used for automatic generation of model consists of 500 subjects. The present screening system provides risk stratification for CAD based on easily available medical data and it produces rules that can be easily interpreted by the medical experts. The developed system is ready to clinically validate on a large dataset.
bioRxiv, 2020
The development of Coronary Artery Disease (CAD), one of the most prevalent diseases in the world, is heavily influenced by several modifiable risk factors. Predictive models built using machine learning (ML) algorithms may assist healthcare practitioners in timely detection of CAD, and ultimately, may improve outcomes. In this study, we have applied six different ML algorithms to predict the presence of CAD amongst patients listed in an openly available dataset provided by the University of California Irvine (UCI) Machine Learning Repository, named “the Cleveland dataset.” All six ML algorithms achieved accuracies greater than 80%, with the “Neural Network” algorithm achieving accuracy greater than 93%. The recall achieved with the “Neural Network” model is also highest of the six models (0.93). Additionally, five of the six algorithms resulted in very similar AUC-ROC curves. The AUC-ROC curve corresponding to the “Neural Network” algorithm is slightly steeper implying higher “true...
An Intelligent Machine Learning Approaches for Predicting Coronary Artery Disease
Coronary Artery Disease (CAD) destroys the internal layer of the artery. Consequently, this destruction leads the fatty sediments to escalate the injury. CAD is one of the common significant reasons of death all around the world, thus early detection of CAD will facilitate scale back these rates. The medical industries gather a large number of facts which include some unknown data to make the choice effective. They also use some excellent data processing methods. The CAD prediction indicates the probability of patients getting artery disease. In this research, we propose various Machine Learning (ML) methods to predict the CAD with the help of historical data. These ML methods enable the system to learn over several datasets to acknowledge valuable understanding. The programmable capability of ML in examining, interpreting, and processing data-set is beneficial to decision-makers in the medical field. This method uses 10 medical parameters to forecast artery disease which is obtained from KEEL (Knowledge Extraction based on Evolutionary Learning). An experiment is performed with algorithms like Naive Bayes, Decision Tree, Neural Network (MLP Classifier), Logistic Regression, and Random Forest with necessary performance metrics like accuracy, precision, recall.
IJERT-Early Detection of Coronary Vascular Disease using Data Mining: Literature Survey
International Journal of Engineering Research and Technology (IJERT), 2014
https://www.ijert.org/early-detection-of-coronary-vascular-disease-using-data-mining-literature-survey https://www.ijert.org/research/early-detection-of-coronary-vascular-disease-using-data-mining-literature-survey-IJERTV3IS080836.pdf In spite of advancements in data mining, the real-time problem remains uncovered and this leads to the wastage of time and economy. Coronary heart disease is the leading cause of mortality in modern society. Although science has made a significance progress in medical diagnosis but the improvement is still needed. And as a human it is our duty to serve patients with proper treatments before they breathe their last,because death is sure. 80% of patients died due to lack of care and care is the child of knowledge. If one could gave us a proper knowledge about the disease it would save thousands of lives. Here we take the support of Data mining and knowledge discovery,because medical science is the best field where these two have proven successful result.We need to again look back and see which technique has proven better results.
Diagnosis of coronary arteries stenosis using data mining
Journal of medical signals and sensors, 2012
Cardiovascular diseases are one of the most common diseases that cause a large number of deaths each year. Coronary Artery Disease (CAD) is the most common type of these diseases worldwide and is the main reason of heart attacks. Thus early diagnosis of CAD is very essential and is an important field of medical studies. Many methods are used to diagnose CAD so far. These methods reduce cost and deaths. But a few studies examined stenosis of each vessel separately. Determination of stenosed coronary artery when significant ECG abnormality exists is not a difficult task. Moreover, ECG abnormality is not common among CAD patients. The aim of this study is to find a way for specifying the lesioned vessel when there is not enough ECG changes and only based on risk factors, physical examination and Para clinic data. Therefore, a new data set was used which has no missing value and includes new and effective features like Function Class, Dyspnoea, Q Wave, ST Elevation, ST Depression and Ti...
Journal of the American Heart Association
Background Rapid coronary plaque progression (RPP) is associated with incident cardiovascular events. To date, no method exists for the identification of individuals at risk of RPP at a single point in time. This study integrated coronary computed tomography angiography–determined qualitative and quantitative plaque features within a machine learning (ML) framework to determine its performance for predicting RPP. Methods and Results Qualitative and quantitative coronary computed tomography angiography plaque characterization was performed in 1083 patients who underwent serial coronary computed tomography angiography from the PARADIGM (Progression of Atherosclerotic Plaque Determined by Computed Tomographic Angiography Imaging) registry. RPP was defined as an annual progression of percentage atheroma volume ≥1.0%. We employed the following ML models: model 1, clinical variables; model 2, model 1 plus qualitative plaque features; model 3, model 2 plus quantitative plaque features. ML ...
A Decision Support System for the Diagnosis of Coronary Artery Disease
19th IEEE Symposium on Computer-Based Medical Systems (CBMS'06), 2006
A rule-based Decision Support System is presented for the diagnosis of Coronary Artery Disease. The generation of the decision support system is realized automatically using a three stage methodology: (a) induction of a decision tree from a training set and extraction of a set of rules; (b) transformation of the set of rules into a fuzzy model and (c) optimization of the parameters of the fuzzy model. The system is evaluated using 199 subjects, each one characterized by 19 features, including demographic and history data, as well as laboratory examinations. Ten fold cross validation was employed and the average sensitivity and specificity obtained was 80% and 65% respectively. Our approach provides diagnosis based on easily acquired features and, since it is rule based, is able to provide interpretation for the decisions made.
Overview on How Data Mining Tools May Support Cardiovascular Disease Prediction
Journal of Applied Computer Science & Mathematics, 2010
Terms as knowledge discovery or Knowledge Discovery from Databases (KDD), Data Mining (DM), Artificial Intelligence (AI), Machine Learning (ML), Artificial Neural networks (ANN), decision tables and trees, gain from day to day, an increasing significance in medical data analysis. They permit the identification, evaluation, and quantification of some less visible, intuitively unpredictable, by using generally large sets of data. Cardiology represents an extremely vast and important domain, having multiple and complex social and human implications. These are enough reasons to promote the researches in this area, becoming shortly not just national or European priorities, but also world-level ones. The profound and multiple interwoven relationships among the cardiovascular risk factors and cardiovascular diseases – but still far to be completely discovered or understood – represent a niche for applying IT&C modern and multidisciplinary tools in order to solve the existing knowledge gaps. This paper’s aim is to present, by emphasizing their absolute or relative pros and cons, several opportunities of applying DM tools in cardiology, more precisely in endothelial dysfunction diagnostic and quantification the relationships between these and so-called “classical” cardiovascular risk factors.
Machine Learning for Diagnosis of Coronary Artery Disease
2019
The main global cause of death is coronary artery disease by the report of the World Health Organization in several years. Furthermore, the medical costs of coronary artery disease are pretty high. The most importantly, heart disease is greatly killed Taiwanese in recent years. Thus, in order to reduce the harm to people, it is necessary to predict coronary artery disease accurately and earlier. The major purpose of this study is to construct different machine learning models on diagnosing of coronary artery disease. The Z-Alizadeh Sani dataset from UCI Machine Learning Repository, including 303 patients and 54 features, was mainly adopted in this study. We apply the 3-fold and 5-fold cross-validation and evaluate the accuracy, sensitivity, specificity, precision, Area Under Curve and Matthews correlation coefficient for different model algorisms, including decision tree, logistic regression and ensemble learning technique. The highly accuracy of 10 features model and 20 features mo...