PREDICTING MORTALITY IN PATIENTS WITH CORONARY ARTERY DISEASE REFERRED TO CARDIAC REHABILITATION: A MACHINE LEARNING APPROACH

Traditionally, Coronary Artery Disease (CAD) is detected only when the person got a heart stroke. The coronary arteries blockages are identified by using a technique called Angiogram test, where a thin wire called catheter is inserted into an artery present in groin or arm and passed through the vessel to reach to heart. Later after identification of coronary artery blockages Angioplasty procedure is done to restore blood flow through the artery. The organ through which blood vessels are clearly visible is Eye. So, our research is in the line of detecting Coronary Artery Disease (CAD) by analysing fundus image of human eye instead of traditional process which is costlier and painful process. Through this technique we can help patients by knowing about the risk of CAD early before having symptoms of heart attack. Machine Learning and Image Processing [10,11] approaches are used in this work of early detection of CAD risk. This is an innovative attempt of detecting Coronary Artery Disease.

Download Full-text

Artificial neural network models for coronary artery disease

Current Bioinformatics ◽

10.2174/1574893615666200214102837 ◽

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.

Download Full-text

The influence of cardiac rehabilitation on heart rate variability indices in men with type 2 diabetes and coronary artery disease

Diabetes and Vascular Disease Research ◽

10.1177/14791641211020184 ◽

2021 ◽

Vol 18 (3) ◽

pp. 147916412110201

Author(s):

Katarzyna Szmigielska ◽

Anna Jegier

Keyword(s):

Type 2 Diabetes ◽

Coronary Artery Disease ◽

Heart Rate ◽

Heart Rate Variability ◽

Coronary Artery ◽

Cardiac Rehabilitation ◽

Interval Training ◽

Patients With Diabetes ◽

Artery Disease

The study evaluated the influence of cardiac rehabilitation (CR) on heart rate variability (HRV) in men with coronary artery disease (CAD) with and without diabetes. Method: The study population included 141 male CAD patients prospectively and consecutively admitted to an outpatient comprehensive CR program. Twenty-seven patients with type-2 diabetes were compared with 114 males without diabetes. The participants performed a 45-min cycle ergometer interval training alternating 4-min workload and a 2-min active restitution three times a week for 8 weeks. The training intensity was adjusted so that the patient’s heart rate achieved the training heart rate calculated according to the Karvonen formula. At the baseline and after 8 weeks, all the patients underwent the HRV assessment. Results: HRV indices in the patients with diabetes were significantly lower as compared to the patients without diabetes in SDNN, TP, LF parameters, both at the baseline and after 8 weeks of CR. After 8 weeks of CR, a significant improvement of TP, SDNN, pNN50% and HF occurred in the patients without diabetes, whereas in the patients with diabetes only HF component improved significantly. Conclusions: As regards HRV indices, CR seems to be less effective in patients with CAD and type-2 diabetes.

Download Full-text

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

Diagnostics ◽

10.3390/diagnostics11030551 ◽

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.

Download Full-text