Machine learning based decision support systems (DSS) for heart disease diagnosis: a review

Safdar, Saima; Zafar, Saad; Zafar, Nadeem; Khan, Naurin Farooq

doi:10.1007/s10462-017-9552-8

Cited by 110 publications

(59 citation statements)

References 26 publications

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“…In other medical fields, a higher number of CDSSs can be found. For example, a review found 60 CDSSs for infectious diseases [ 46 ], while a systematic review in cardiology identified 331 relevant studies [ 47 ]. This might have been caused by the fact that machine learning for RDs is currently a problem due to a lack of data pertaining to RD patients.…”

Section: Discussionmentioning

confidence: 99%

Diagnosis of Rare Diseases: a scoping review of clinical decision support systems

Schaaf

Sedlmayr

Schaefer

et al. 2020

Orphanet J Rare Dis

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Background Rare Diseases (RDs), which are defined as diseases affecting no more than 5 out of 10,000 people, are often severe, chronic and life-threatening. A main problem is the delay in diagnosing RDs. Clinical decision support systems (CDSSs) for RDs are software systems to support clinicians in the diagnosis of patients with RDs. Due to their clinical importance, we conducted a scoping review to determine which CDSSs are available to support the diagnosis of RDs patients, whether the CDSSs are available to be used by clinicians and which functionalities and data are used to provide decision support. Methods We searched PubMed for CDSSs in RDs published between December 16, 2008 and December 16, 2018. Only English articles, original peer reviewed journals and conference papers describing a clinical prototype or a routine use of CDSSs were included. For data charting, we used the data items “Objective and background of the publication/project”, “System or project name”, “Functionality”, “Type of clinical data”, “Rare Diseases covered”, “Development status”, “System availability”, “Data entry and integration”, “Last software update” and “Clinical usage”. Results The search identified 636 articles. After title and abstracting screening, as well as assessing the eligibility criteria for full-text screening, 22 articles describing 19 different CDSSs were identified. Three types of CDSSs were classified: “Analysis or comparison of genetic and phenotypic data,” “machine learning” and “information retrieval”. Twelve of nineteen CDSSs use phenotypic and genetic data, followed by clinical data, literature databases and patient questionnaires. Fourteen of nineteen CDSSs are fully developed systems and therefore publicly available. Data can be entered or uploaded manually in six CDSSs, whereas for four CDSSs no information for data integration was available. Only seven CDSSs allow further ways of data integration. thirteen CDSS do not provide information about clinical usage. Conclusions Different CDSS for various purposes are available, yet clinicians have to determine which is best for their patient. To allow a more precise usage, future research has to focus on CDSSs RDs data integration, clinical usage and updating clinical knowledge. It remains interesting which of the CDSSs will be used and maintained in the future.

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Section: Discussionmentioning

confidence: 99%

Diagnosis of Rare Diseases: a scoping review of clinical decision support systems

Schaaf

Sedlmayr

Schaefer

et al. 2020

Orphanet J Rare Dis

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“…Unfortunately, the lack of training sets, limits the deep learning method. Nevertheless within a few years, the introduction of better training sets will effectively predict retinal pathologies and decrease OCT technology costs [24].…”

Section: Medicinementioning

confidence: 99%

Deep Learning: A Review

Vargas¹,

Mosavi²,

Ruíz³

2018

Preprint

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Deep learning is an emerging area of machine learning (ML) research. It comprises multiple hidden layers of artificial neural networks. The deep learn- ing methodology applies nonlinear transformations and model abstractions of high level in large databases. The recent advancements in deep learning architec- tures within numerous fields have already provided significant contributions in artificial intelligence. This article presents a state of the art survey on the contri- butions and the novel applications of deep learning. The following review chron- ologically presents how and in what major applications deep learning algorithms have been utilized. Furthermore, the superior and beneficial of the deep learning methodology and its hierarchy in layers and nonlinear operations are presented and compared with the more conventional algorithms in the common applica- tions. The state of the art survey further provides a general overview on the novel concept and the ever-increasing advantages and popularity of deep learning.

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“…Out of the three techniques, supervised learning is the most widely used (56). The role of machine learning in the field of medicine, particularly cardiology, has been covered in several papers (57,58). Various supervised learning algorithms including support vector machines, decision trees, random forests, k-nearest neighbors, and others, have been applied to problems ranging from image analysis, prediction, diagnosis and treatment of heart disease (57).…”

Section: Cadx and Machine Learningmentioning

confidence: 99%

Deep learning for cardiac computer-aided diagnosis: benefits, issues & solutions

Loh¹,

Then²

2017

mHealth

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Cardiovascular diseases are one of the top causes of deaths worldwide. In developing nations and rural areas, difficulties with diagnosis and treatment are made worse due to the deficiency of healthcare facilities. A viable solution to this issue is telemedicine, which involves delivering health care and sharing medical knowledge at a distance. Additionally, mHealth, the utilization of mobile devices for medical care, has also proven to be a feasible choice. The integration of telemedicine, mHealth and computer-aided diagnosis systems with the fields of machine and deep learning has enabled the creation of effective services that are adaptable to a multitude of scenarios. The objective of this review is to provide an overview of heart disease diagnosis and management, especially within the context of rural healthcare, as well as discuss the benefits, issues and solutions of implementing deep learning algorithms to improve the efficacy of relevant medical applications.

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Machine learning based decision support systems (DSS) for heart disease diagnosis: a review

Cited by 110 publications

References 26 publications

Diagnosis of Rare Diseases: a scoping review of clinical decision support systems

Diagnosis of Rare Diseases: a scoping review of clinical decision support systems

Deep Learning: A Review

Deep learning for cardiac computer-aided diagnosis: benefits, issues & solutions

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