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| Citation | Method | Description |
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| [6] | Wearable technology for cardiology | Devices of wearable technology were intended for measuring the heart rhythm, heart rate, activity, and thoracic fluid. For classification and understanding, a framework was given to the wearable devices for improving healthcare. |
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| [16] | Technology for cardiovascular disease patient | 475 papers from PubMed library were examined for solutions of telemedicine to improve medication adherence of cardiovascular disease patient. 74 articles were assessed. The articles exhibited that suggestions associated with solutions of telemedicine are typically conflictive. The use of SMS was considered for patients regarding their medication because of forgetfulness. |
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| [17] | Big data for monitoring of proactive healthcare of patients of chronic diseases | A system of health proactive monitoring is planned for cardiac patients. In the study, an electronic band is worn by the patient and the real-time situation of health and system of e-health are to process the obtained data from patient. The system supports the patient to take proactive process beside some of the abnormal states in their health and further supports the doctors to monitor the health of patient on a regular basis. |
| [18] | Deep learning network in left ventricular volumes identification in cardiac MRI | The research has established left ventricular volumes approach of identification without segmentation by deep learning technology and the data set form large-scale cardiac MRI from second annual data science bowl in 2016 |
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| [19] | Medical data mining and heart disease | The study offered review of the associated techniques of data mining for finding of diseases classes and heart failure. Additionally, emphasis is given on sequential mining. |
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| [20] | ML horizon in cardiac hybrid imaging | The study presented summary of the fundamental notions in ML and its applications in standard cardiac imaging |
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| [21] | Mobile heart rate monitoring system for patient of MI | Application of mobile for the patient of myocardial infarction (MI) is presented to preserve heart rate tracking and with stress-free pursuing for emergency support |
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| [22] | Clinical guidelines in cardiology run-through in Sudan | Interviews in the two main cardiac hospitals of Sudan were led and an exploratory study among the hospitals’ doctors was prepared for examining the perceptions of a huge population of prescribers of the subject examined |
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| [23] | Big health data | Management of cardiac data and operative concept of remodelling to examine early symptoms of heart failure is presented |
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| [24] | Big health data records for early and late translational cardiovascular research | The research censoriously reviewed the challenges of big data before time and after the event stages of research in translational cardiovascular disease |
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| [25] | Factorization of tensor for precision medicine in failure of heart with preserved ejection fraction | The study examined the related woks on factorization of tensor applications in the associated biomedical field of phenotyping and genotyping |
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| [26] | Mobile messaging applications’ effect on knowledge of cardiac patients with risk of coronary artery disease and healthy lifestyle adherence | The research led a study from January to April 2017 in Klang Valley’s teaching hospital for determining the effect of mobile messaging applications on patients with coronary artery disease and observation of healthy life |
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| [27] | SVM for classification of biomedical signal on IoT platform | The study observed the signal over digital signal processor and then computed the blood oxygen saturation, heart rate, and blood pressure. SVM was considered for the purpose of showing the data and its classification into unhealthy, healthy, and very unhealthy and designates accuracy of classification prediction. |
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| [28] | Heart failure prevention | Risk factors of heart failure and focus on prevention are specified |
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| [29] | Statistical shape models of the heart | The study presented summary of the collected works of statistical shape models in cardiac imaging |
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| [30] | Classifying mining techniques for the accessible clarification for heart disease prediction | Framework to use healthcare data for attributes based heart disease prediction is presented in the study |
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| [31] | Statistical based recommendation model for the patients of heart disease | Smart system of recommendation is presented for patients with heart disease in the fields of medical informatics and e-health |
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| [32] | Modelling 4D for rapid assessment of biventricular function in congenital heart disease | The study has quantified 4D biventricular function from analysis of standard cardiac MRI |
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| [33] | Big database applications and forthcomings in cardiology | Applications of big database studies in cardiology were presented in the study |
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| [34] | Approach of big data to myocyte membrane analysis | The study presented an approach for identification of particular pathological ion dynamics responsible for abnormal electrical behaviour practiced through the experiment |
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| [35] | Distance, quality, or relationship? Interhospital transfer of patients with heart attack | The research inspected the patterns where the patients of heart attack are transferred between the hospitals. The three key factors in transferring destinations are: |
| (i) The distance between sending and receiving in hospitals |
| (ii) Widely reported quality measures of receiving hospitals as specified by whether they are related with the same multihospital system |
| (iii) The relationship between sending and receiving hospitals |
| [36] | Feature analysis and coronary artery heart disease data sets | Integrate the experimental results of the examination of ML which are applied on varied data sets aiming at the coronary artery heart disease |
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| [37] | Mapping of ventricular tachycardia in patients with heart structural disease | The paper has focused on the procedure of mapping ventricular tachycardia; the conventional and novel technique of mapping and the details of some methodological tips are given |
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| [38] | Patients’ baseline characteristics with heart failure and preserved ejection fraction during admission with acute heart failure in Saudi Arabia | Saudi Arabian patients with HFpEF were examined with acute heart failure. The clinical characteristics, signs, and indications of heart failure, echocardiographic findings, and medications during admission and at hospital discharge were determined. |
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| [39] | Cardiovascular dysautonomias diagnosis and treatments through data mining | The authors established a cardiovascular dysautonomias identification system for the prediction of appropriate treatments and diagnosis for patients with cardiovascular dysautonomias through the data set extracted from the ANS unit of University Hospital Avicenne in Morocco |
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| [40] | Approach of data transmission based on adaptive energy efficiency for prediction of heart disease | The research developed an adaptive energy resourceful transmission system which can recognise the important events like myocardial infarction and reduce data transmission from the devices |
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| [41] | Analytics of big data in prediction of heart attack | The study identified the analytics uses in big data for the prevention and prediction of heart attack, privacy of the patients, and the challenges for the use of technology in big data. The study analyzed the national and international databases for the proposed study. |
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| [42] | Cloud computing for myocardial fibre information in vivo | System of cloud-based investigation is intended for cardiac images and link services of computation for remote sharing. A method for postprocessing of image is defined as important service for obtaining information on in vivo myocardial fibres. |
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| [43] | Using big data for assessing the risks of arrhythmia | The research presented an algorithm to involuntarily identify the R, S, and T wave peaks in epicardial electrogram signals |
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| [44] | ML framework and imaging based big data for rapid phenotyping of left ventricular diastolic function | The study proposed that the cardiac biomechanics produce adequate information which can affect ML and framework of big data analytics for function of automated left ventricular diastolic assessment |
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| [45] | Insights from echo reports of paediatric disease of heart | The entity site-feature values are mined in triples in the report of echo and then on the ground of this prediction of the level of risk |
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| [46] | Framework of probabilistic data driven for scoring the preoperatives recipient-donor heart transplant survival | The technique of Bayesian belief networks is used. The approach contains four phases; the first and second phases of the data are preprocessed and a set of predictors are produced based on different variable selection method. The medically associated variables are added to the list of variables in the third phase, and in the last phase the Bayesian belief networks technique is applied. |
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| [47] | Identification of heart arrhythmia through big data-based extraction of fuzzy partition rules | The research presented a novel semiautomatically fuzzy partition rules for facilitating an accurate and robust aspect into cardiac arrhythmia. The approach of text mining is demonstrated and applied to large data set containing freely existing articles in the PubMed library. The information is mined and then put to the experimental data and expert information for facilitating robust system to tackle the issues arising through the assessment of medical big data. |
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| [48] | Big data for prediction of heart disease through map reduction | The research has established a central monitoring system for patients of large set of health records as input. It is intended to mine the essential information from large set of medical records through the method of map reduction. By using this approach, it can be decided whether there is patient normality or abnormality. |
| [49] | Cardiovascular risk clustering factors highlighted the coronary artery calcium as a strong clinical discriminator | The authors studied the relations between cardiovascular risk clustering and the discriminators of disease of cardiovascular factors |
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| [50] | Mobile health initiatives for cardiovascular disease | The current technological and clinical improvements containing wearable health tracking devices, smartphone devices, and social media for supporting behaviour factors of risk for cardiovascular disease in terms of smoking, physical inactivity, and suboptimal nutrition |
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| [51] | Sudden cardiac death with risk stratification and computational cardiology | The study defined guidelines of what is to be required for making the translational step, through the comparatively well intended cases required or drug induced long QT as a case of syndrome |
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| [52] | Technology of smartphone in cardiology | The research presented various applications of smartphone based technologies in cardiology and gave a review of them |
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| [53] | Visualization of cardiovascular MRI challenges and opportunities | The study offered an overview of the existing associated works of visualization approaches and emphasis on the visualizing imagery issues resulting from 2D myocardial tagging in CMR |
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| [54] | Big data in cardiology | The article’s purpose is the three encouraging big data applications in cardiovascular care, with “proof-of-concept” challenges to be met if the encouraging data is to be comprehended |
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| [55] | Cardiovascular medicine big data, health informatics, and future | The study offered a report on cardiovascular medicine big data, health informatics, and future |
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| [56] | Tool for the MIMIC-II database, a web-based data visualization | The objectives of the study are:
(a) to build an interactive and
(b) data visualization tool based on web MIMIC-II
Furthermore, the research mainly offered two features of exploration and comparison. The first feature helps the patient cohort within MIMIC-II and visualized the distribution of various variables including administrative, clinical, and demographic variables within the selected cohort. The second feature helps the users in selection of two patient cohorts and visual comparison with other variables. |
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| [57] | Connecting the dots: from big data to healthy heart | The study designated various sources of big data in cardiology followed by talk over the possibilities of building the best use of data-driven knowledge production models |
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| [58] | Libraries implementation of open-source data visualization of web portal for patients of diabetes | A web portal is employed for improved communications of diabetes patients with doctors for the process of identification and handling of diabetes. Medical data are offered on the portal based on open-source libraries. |
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| [52] | Technology of smartphone in cardiology | The research discusses the details of diverse applications of technologies of smartphone in cardiology |
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| [59] | Machine learning approaches in detection of ischemic heart disease | SVM and Osuna were used for detecting the ischemic disease of heart. The principal component analysis algorithm was also used. |
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| [60] | Paediatric cardiovascular disease in the era of transparency in healthcare using big data | The research offered a review on analytics of big data impact in paediatric cardiovascular disease and its possible issues of transparency in distribution of care |
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| [61] | Data visualization: science on the map | A tool box for data visualization |
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| [62] | Harnessing the heart of big data | The paper discussed the following: |
| (i) Report on big data science research |
| (ii) Potential of data science to support examinations of cardiovascular diseases |
| (iii) Challenges and opportunities |
|
| [63] | 4D OCT in developmental cardiology | The chapter emphasizes on numerous existing solutions and gives review of the perspective in the evaluation of 4D OCT imaging for cardiovascular system in the past several years |
| [64] | Kinect-based gesture prediction in volumetric visualization of heart from CMR imaging | The research aims to offer a virtual human heart from medical imaging data with incorporation of collaborating interface using visual 3D holographic, haptic, and sonic feedback |
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| [65] | Feast for the eyes | The research presented the existing data visualization uses and reviewed the probable issues, benefits, and applications of libraries |
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| [66] | Cardiac 4D ultrasound imaging | Overview of the technological developments for volumetric imaging of the heart beat with the support of ultrasound is given |
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| [67] | Probabilistic data-driven framework for scoring the preoperative recipient-donor heart transplant survival | The study presented Bayesian belief network containing four phases. The data is preprocessed in the first two phases and produces a candidate set of predictors. Medical related variables are added in the third phase and, finally, the model of Bayesian belief network is applied. |
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| [68] | Health analytics | The chapter discussed the visualization, analysis, and mining of healthcare data and concludes the way in which data can be proficiently accomplished which further improves the ability of organization to control risk, yield revenue, and cost |
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| [69] | Electrophysiology-morphous merging of human heart based on composite visualization approach | The paper presented cardiac electrical excitation propagation model based on the data of human cardia cross-sectional to discover the cardiac electrical activities. After that, biophysical visualization method is applied for the biophysical integration of cardiac anatomy and electrophysiological properties, which provide the equivalent position, spatial relationship, and the whole process in 3D space with the context of anatomical structure for giving the details of biophysical and electrophysiological activity. |
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| [70] | Big data for cardiology: novel discovery? | The paper determined the encouraging data sets for finding of science and the impact on the approaches used in science in general and explicitly in cardiology |
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| [71] | Visualization of medical volume though intelligent approaches | The uses of algorithms and intelligent approaches of visualizing medical big data are presented. The article discusses the existing software and toolkits for visualization of medical volume. |
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