scholarly journals Early Prediction of Coronary Artery Disease (CAD) by Machine Learning Method - A Comparative Study

2021 ◽  
Vol 3 (1) ◽  
pp. 17-33
Author(s):  
Joy Iong Zong Chen ◽  
Hengjinda P
Keyword(s):  
Machine Learning ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Learning Algorithm ◽  
Early Prediction ◽  
Machine Learning Method ◽  
Learning Method ◽  
Medical Field ◽  
Knowledge Based ◽  
Artery Disease

Coronary Artery Disease (CAD) prediction is a very hard and challenging task in the medical field. The early prediction in the medical field especially the cardiovascular sector is one of the virtuosi. The prior studies about the construction of the early prediction model developed an understanding of the recent techniques to find the variation in medical imaging. The prevention of cardiovascular can be fulfilled through a diet chart prepared by the concerned physician after early prediction. Our research paper consists of the prediction of CAD by the proposed algorithm by constructing of pooled area curve (PUC) in the machine learning method. This knowledge-based identification is an important factor for accurate prediction. This significant approach provides a good impact to determine variation in medical images although weak pixels surrounding it. This pooled area construction in our machine learning algorithm is bagging shrinking veins and tissues with the help of clogging and plaque of blood vessels. Besides, the noisy type database is used in this article for better clarity about identifying the classifier. This research article provides the recent adaptive image-based classification techniques and it comparing existing classification methods to predict CAD earlier for a higher accurate value. This proposed method is taking as evidence to diagnosis any heart disease earlier. The decision-making of classified output provides better accurate results in our proposed algorithm.

Artificial neural network models for coronary artery disease

2020 ◽  
Vol 15 ◽  
Author(s):  
Elham Shamsara ◽  
Sara Saffar Soflaei ◽  
Mohammad Tajfard ◽  
Ivan Yamshchikov ◽  
Habibollah Esmaili ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Coronary Artery Disease ◽  
Pattern Recognition ◽  
Artificial Neural Network ◽  
Coronary Artery ◽  
Diagnostic Model ◽  
Early Prediction ◽  
Artificial Neural ◽  
Artery Disease

Background: Coronary artery disease (CAD) is an important cause of mortality and morbidity globally. Objective : The early prediction of the CAD would be valuable in identifying individuals at risk, and in focusing resources on its prevention. In this paper, we aimed to establish a diagnostic model to predict CAD by using three approaches of ANN (pattern recognition-ANN, LVQ-ANN, and competitive ANN). Methods: One promising method for early prediction of disease based on risk factors is machine learning. Among different machine learning algorithms, the artificial neural network (ANN) algo-rithms have been applied widely in medicine and a variety of real-world classifications. ANN is a non-linear computational model, that is inspired by the human brain to analyze and process complex datasets. Results: Different methods of ANN that are investigated in this paper indicates in both pattern recognition ANN and LVQ-ANN methods, the predictions of Angiography+ class have high accuracy. Moreover, in CNN the correlations between the individuals in cluster ”c” with the class of Angiography+ is strongly high. This accuracy indicates the significant difference among some of the input features in Angiography+ class and the other two output classes. A comparison among the chosen weights in these three methods in separating control class and Angiography+ shows that hs-CRP, FSG, and WBC are the most substantial excitatory weights in recognizing the Angiography+ individuals although, HDL-C and MCH are determined as inhibitory weights. Furthermore, the effect of decomposition of a multi-class problem to a set of binary classes and random sampling on the accuracy of the diagnostic model is investigated. Conclusion : This study confirms that pattern recognition-ANN had the most accuracy of performance among different methods of ANN. That’s due to the back-propagation procedure of the process in which the network classify input variables based on labeled classes. The results of binarization show that decomposition of the multi-class set to binary sets could achieve higher accuracy.

Machine learning algorithm-based risk prediction model of coronary artery disease

2018 ◽  
Vol 45 (5) ◽  
pp. 901-910 ◽  
Author(s):  
Shaik Mohammad Naushad ◽  
Tajamul Hussain ◽  
Bobbala Indumathi ◽  
Khatoon Samreen ◽  
Salman A. Alrokayan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Prediction Model ◽  
Risk Prediction ◽  
Learning Algorithm ◽  
Risk Prediction Model ◽  
Machine Learning Algorithm ◽  
Artery Disease

scholarly journals Machine Learning Quantitation of Cardiovascular and Cerebrovascular Disease: A Systematic Review of Clinical Applications

Diagnostics ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 551
Author(s):  
Chris Boyd ◽  
Greg Brown ◽  
Timothy Kleinig ◽  
Joseph Dawson ◽  
Mark D. McDonnell ◽  
...  
Keyword(s):  
Machine Learning ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Model Comparison ◽  
Disease Risk ◽  
Imaging Modalities ◽  
Current Status ◽  
Advantages And Disadvantages ◽  
Body Regions ◽  
Artery Disease

Research into machine learning (ML) for clinical vascular analysis, such as those useful for stroke and coronary artery disease, varies greatly between imaging modalities and vascular regions. Limited accessibility to large diverse patient imaging datasets, as well as a lack of transparency in specific methods, are obstacles to further development. This paper reviews the current status of quantitative vascular ML, identifying advantages and disadvantages common to all imaging modalities. Literature from the past 8 years was systematically collected from MEDLINE® and Scopus database searches in January 2021. Papers satisfying all search criteria, including a minimum of 50 patients, were further analysed and extracted of relevant data, for a total of 47 publications. Current ML image segmentation, disease risk prediction, and pathology quantitation methods have shown sensitivities and specificities over 70%, compared to expert manual analysis or invasive quantitation. Despite this, inconsistencies in methodology and the reporting of results have prevented inter-model comparison, impeding the identification of approaches with the greatest potential. The clinical potential of this technology has been well demonstrated in Computed Tomography of coronary artery disease, but remains practically limited in other modalities and body regions, particularly due to a lack of routine invasive reference measurements and patient datasets.

scholarly journals MACHINE LEARNING FOR PREDICTING ANGIOGRAPHIC PRESENCE OF ATHEROSCLEROTIC CORONARY ARTERY DISEASE IN YOUNG ADULTS

2021 ◽  
Vol 77 (18) ◽  
pp. 65
Author(s):  
Maryam Saleem ◽  
Naveena Yanamala ◽  
Irfan Zeb ◽  
Brijesh Patel ◽  
Heenaben Patel ◽  
...  
Keyword(s):  
Machine Learning ◽  
Coronary Artery Disease ◽  
Young Adults ◽  
Coronary Artery ◽  
Artery Disease

scholarly journals Performance Prediction and Optimization of Solar Water Heater via a Knowledge-Based Machine Learning Method

2018 ◽  
pp. 55-74 ◽  
Author(s):  
Hao Li ◽  
Zhijian Liu
Keyword(s):  
Machine Learning ◽  
Machine Learning Method ◽  
Solar Water Heater ◽  
Learning Method ◽  
Optimization Strategy ◽  
Learning Models ◽  
Water Heater ◽  
Solar Water ◽  
Knowledge Based ◽  
Machine Learning Models

Measuring the performance of solar energy and heat transfer systems requires a lot of time, economic cost, and manpower. Meanwhile, directly predicting their performance is challenging due to the complicated internal structures. Fortunately, a knowledge-based machine learning method can provide a promising prediction and optimization strategy for the performance of energy systems. In this chapter, the authors show how they utilize the machine learning models trained from a large experimental database to perform precise prediction and optimization on a solar water heater (SWH) system. A new energy system optimization strategy based on a high-throughput screening (HTS) process is proposed. This chapter consists of: 1) comparative studies on varieties of machine learning models (artificial neural networks [ANNs], support vector machine [SVM], and extreme learning machine [ELM]) to predict the performances of SWHs; 2) development of an ANN-based software to assist the quick prediction; and 3) introduction of a computational HTS method to design a high-performance SWH system.

P6435A diagnostic prediction model of coronary artery disease in patient with chest pain using machine learning

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
S W Rha ◽  
B G Choi ◽  
S Y Choi ◽  
J K Byun ◽  
J A Cha ◽  
...  
Keyword(s):  
Machine Learning ◽  
Coronary Artery Disease ◽  
Chest Pain ◽  
Coronary Artery ◽  
Prediction Model ◽  
Roc Analysis ◽  
Learning Model ◽  
Coronary Syndrome ◽  
Artery Disease ◽  
Registry Database

Abstract Background Chest pain is a major symptom of coronary artery disease (CAD), which can lead to acute coronary syndrome and sudden cardiac death. Accurate diagnosis of CAD in patients who experience chest pain is crucial to provide appropriate treatment and optimize clinical outcomes. Objective This study was to develop a machine learning model which can predict and diagnose CAD in patients complaining of chest pain based on a large real-world prospective registry database and computing power. Method A total of 10,177 subjects with typical or atypical chest pain who underwent a coronary angiography at the cardiovascular center of our University Hospital, South Korea between November 2004 and May 2014 were evaluated in this study. The generation of the diagnostic prediction model for CAD used the classification application by technical support of MATLAB R2017a. The performance evaluation of the learning model generated by machine learning was evaluated by the area under the curve (AUC) of the receiver-operating characteristic (ROC) analysis. Results The diagnostic prediction model of CAD had been generated according to the user's accessibility such as the general public or clinician (Model 1–4). The performance of the models has ranged from 0.78 to 0.96 by the AUC of ROC analysis. The prediction accuracy of the models ranged from 70.4% to 88.9%. The performance of the diagnostic prediction model of CAD by machine learning improved as the input information increased. Figure 1. Study Flow Chart Conclusion A diagnostic prediction model of CAD using the machine learning method and the registry database was developed. Further studies are needed to verify our results.

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