Coronary artery disease detection using artificial intelligence techniques: A survey of trends, geographical differences and diagnostic features 1991-2020.

The innovative technologies emerged with the industrial revolution “Industry 4.0” as well as the new ones on the way of advanced digitalization enable delivering enhanced, value-added and cost-effective manufacturing and service operations. One of the first areas of focus for Industry 4.0 applications is operations related to healthcare services. Effective management of healthcare resources, clinical care processes, service planning, delivery and evaluation of healthcare operations are essential for a well-functioning healthcare system. Yet, with the adoption of technologies such as Internet of Health Things, Medical Cyber–Physical Systems, Machine Learning, and Big Data (BD), the healthcare sector has recognized the relevance of Industry 4.0. The concept of BD offered numerous advantages and opportunities in this field. It changed the way information is gathered, shared and utilized. Hence, in this study our main ambition is to provide readers with a review of publications which lie within the intersection of Industry 4.0, BD, and healthcare operations and give future perspectives. Our review shows that BD constitutes an important place on the technologies Industry 4.0 provides in the healthcare domain. It helps design, improve, analyze, assess and optimize operations in the domain. 

Big Data for Healthcare Industry 4.0: Applications, challenges and future perspectives

Myocarditis is the form of an inflammation of the middle layer of the heart wall which is caused by a viral infection and can affect the heart muscle and its electrical system. It has remained as one of the most challenging diagnoses in cardiology. Myocardial is the prime cause of unexpected death in approximately 20% of adults less than 40 years of age. Cardiac MRI (CMR) has been considered as a noninvasive and golden standard diagnostic tool for suspected myocarditis and plays an indispensable role in diagnosing various cardiac diseases. However, the performance of CMR is heavily dependent on the clinical presentation and non-specific features such as chest pain, arrhythmia, and heart failure. Besides, other imaging factors like artifacts, technical errors, pulse sequence, acquisition parameters, contrast agent dose, and more importantly qualitatively visual interpretation can affect the result of the diagnosis. This paper introduces a new deep learning-based model called Convolutional Neural Network-Clustering (CNN-KCL) to diagnose the Myocarditis. The hybrid CNN-KCL method performs the early and accurate diagnosis of Myocarditis. To the best-of-our-knowledge, a Convolutional neural network has never been used before for the diagnosis of Myocarditis. In this study, we used 47 subjects to diagnose myocarditis patients from Tehran's Omid Hospital. The total number of data examined is 10425. Our results demonstrate that CNNKCL achieves 92.3% in terms of diagnosis myocarditis prediction accuracy which is significantly better than those reported in previous studies.

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CNN-KCL: Automatic Myocarditis Diagnosis using Convolutional Neural Network Combined with K-means Clustering

One of the most common and critical destructive attacks on the victim system is the advanced persistent threat (APT)-attack. An APT attacker can achieve its hostile goal through obtaining information and gaining financial benefits from the infrastructure of a network. One of the solutions to detect a unanimous APT attack is using network traffic. Due to the nature of the APT attack in terms of being on the network for a long time and the fact that the system may crash due to the high traffic, it is difficult to detect this type of attack. Hence, in this study, machine learning methods of C5.0 decision tree, Bayesian network, and deep learning are used for the timely detection and classification of APT-attacks on the NSL-KDD dataset. Moreover, a 10-fold cross-validation method is used to experiment with these models. As a result, the accuracy (ACC) of the C5.0 decision tree, Bayesian network, and 6-layer deep learning models is obtained as 95.64%, 88.37%, and 98.85%, respectively. Also, in terms of the critical criterion of the false positive rate (FPR), the FPR value for the C5.0 decision tree, Bayesian network, and 6-layer deep learning models is obtained as 2.56, 10.47, and 1.13, respectively. Other criterions such as sensitivity, specificity, accuracy, false-negative rate, and F-measure are also investigated for the models, and the experimental results show that the deep learning model with automatic multi-layered extraction of features has the best performance for timely detection of an APT-attack comparing to other classification models.

Early Detection of the Advanced Persistent Threat Attack Using Performance Analysis of Deep Learning

One of the leading causes of deaths around the globe is heart disease. Heart is an organ that is responsible for the supply of blood to each part of the body. Coronary artery disease (CAD) and chronic heart failure (CHF) often lead to heart attack. Traditional medical procedures (angiography) for the diagnosis of heart disease have higher cost as well as serious health concerns. Therefore, researchers have developed various automated diagnostic systems based on machine learning (ML) and data mining techniques. ML-based automated diagnostic systems provide an affordable, efficient, and reliable solutions for heart disease detection. Various ML, data mining methods, and data modalities have been utilized in the past. Many previous review papers have presented systematic reviews based on one type of data modality. This study, therefore, targets systematic review of automated diagnosis for heart disease prediction based on different types of modalities, i.e., clinical feature-based data modality, images, and ECG. Moreover, this paper critically evaluates the previous methods and presents the limitations in these methods. Finally, the article provides some future research directions in the domain of automated heart disease detection based on machine learning and multiple of data modalities.

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Machine Learning-Based Automated Diagnostic Systems Developed for Heart Failure Prediction Using Different Types of Data Modalities: A Systematic Review and Future Directions

Heart disease is one of the most common diseases in middle-aged citizens. Among the vast number of heart diseases, 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, we propose an integrated method using machine learning. The machine learning methods of random trees (RTs), decision tree of C5.0, support vector machine (SVM), and decision tree of Chi-squared automatic interaction detection (CHAID) are used in this study. The proposed method shows promising results and the study confirms that the RTs model outperforms other models.

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Coronary artery disease diagnosis; ranking the significant features using a random trees model

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Edris Hassannataj Joloudari

Papers

Coronary artery disease diagnosis; ranking the significant features using a random trees model

Coronary Artery Disease Diagnosis: Ranking the Significant Features Using Random Trees Model