Machine-learning Algorithms for Ischemic Heart Disease Prediction:
A Systematic Review

Hani, Salam Bani; Ahmad, Muayyad

doi:10.2174/1573403x18666220609123053

Cited by 29 publications

(14 citation statements)

References 28 publications

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“…Two studies used big data to reduce health disparities and improve the ability of health providers to meet predictable demand, 11,18 as well as to assess nursing leadership and decision-making. The results showed that big data can create new transformational leaders for nurses to efficiently use 21st-century health systems.…”

Section: Recommendation\considerationsmentioning

confidence: 99%

“…Nurses must collect and analyze data in today's knowledge-based society. 10,11 In light of the evolution of nursing in the context of big data, big data centers for nursing science, advanced professional nursing databases, and a knowledge system structure are needed. 9 Big data can predict blueprints and patterns shared by thousands of people and evaluate multiple data sources collected by nurses and other health care professionals.…”

Section: Introductionmentioning

confidence: 99%

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Big data can help prepare nurses and improve patient outcomes by improving quality, safety, and outcomes

Ahmad

Hani

Sabra

et al. 2023

Frontiers of Nursing

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Objectives Big data has revolutionized nursing and health care and raised concerns. This research aims to help nurses understand big data sets to provide better patient care. Methods This study used big data in nursing to improve patient care. Big data in nursing has sparked a global revolution and raised concerns, but few studies have focused on helping nurses understand big data to provide the best patient care. This systematic review was conducted based on PRISMA guidelines. PubMed, MEDLINE, CINAHL, Google Scholar, and ResearchGate were used for 2010–2020 studies. Results The most common use of big data in nursing was investigated in eight papers between 2015 and 2018. All research showed improvements in patient outcomes and healthcare delivery when big data was used in the medical-surgical, emergency department, critical care unit, community, systems biology, and leadership applications. Big data is not taught to nurses. Conclusions Big data applications in nursing and health care improve early intervention and decision-making. Big data provides a comprehensive view of a patient’s status and social determinants of health, allowing treatment using all metaparadigms and avoiding a singular focus. Big data can help prepare nurses and improve patient outcomes by improving quality, safety, and outcomes.

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Section: Recommendation\considerationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Big data can help prepare nurses and improve patient outcomes by improving quality, safety, and outcomes

Ahmad

Hani

Sabra

et al. 2023

Frontiers of Nursing

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“…Hani and Ahmad (2020) conducted a review of several machine learning algorithms that have the potential to detect ischemic heart disease. The authors present a comprehensive summary of various algorithms, including logistic regression, artificial neural networks, decision trees and support vector machines.…”

Section: Adoption Of Machine Learning In the Health Sectormentioning

confidence: 99%

Adoption of machine learning systems within the health sector: a systematic review, synthesis and research agenda

Bundi

2023

DTS

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PurposeThe purpose of this study is to examine the state of research into adoption of machine learning systems within the health sector, to identify themes that have been studied and observe the important gaps in the literature that can inform a research agenda going forward.Design/methodology/approachA systematic literature strategy was utilized to identify and analyze scientific papers between 2012 and 2022. A total of 28 articles were identified and reviewed.FindingsThe outcomes reveal that while advances in machine learning have the potential to improve service access and delivery, there have been sporadic growth of literature in this area which is perhaps surprising given the immense potential of machine learning within the health sector. The findings further reveal that themes such as recordkeeping, drugs development and streamlining of treatment have primarily been focused on by the majority of authors in this area.Research limitations/implicationsThe search was limited to journal articles published in English, resulting in the exclusion of studies disseminated through alternative channels, such as conferences, and those published in languages other than English. Considering that scholars in developing nations may encounter less difficulty in disseminating their work through alternative channels and that numerous emerging nations employ languages other than English, it is plausible that certain research has been overlooked in the present investigation.Originality/valueThis review provides insights into future research avenues for theory, content and context on adoption of machine learning within the health sector.

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“…For example, a study developed and validated a coronary artery disease-predictive machine learning model using electronic health records and assessed its probabilities as in silico scores for coronary artery disease in participants in two longitudinal biobank cohorts [10]. Another study applied an ensemble ML model for coronary disease prediction, using ML classi ers to predict heart disease [11].…”

Section: Introductionmentioning

confidence: 99%

Unveiling Coronary Heart Disease Prediction through Machine Learning Techniques: Insights from the Suita Population-Based Cohort Study

Vu,

Kokubo,

Inoue

et al. 2024

Preprint

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We leveraged machine learning (ML) techniques, namely logistic regression (LR), random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost), and LightGBM to predict coronary heart disease (CHD) and identify the key risk factors involved. Based on the Suita study, 7672 men and women aged 30 to 84 years without cardiovascular disease were recruited from 1989 to 1999, in Suita City, Osaka, Japan. Over an average period of 15 years, participants were diligently monitored until the onset of their initial cardiovascular event or relocation. CHD diagnoses encompassed primary heart attacks, sudden death, or coronary artery disease with bypass surgery or intervention. RF achieved the highest AUC (95% CI) of 0.79 (0.70–0.87), outperforming LR, SVM, XGBoost, and LightGBM. Shapley Additive Explanations (SHAP) on the best model identified the top CHD predictors. Notably, systolic blood pressure, non-HDL-c, glucose levels, age, metabolic syndrome, HDL-c, estimated glomerular filtration rate, hypertension, elbow joint thickness, and diastolic blood pressure were key contributors. Remarkably, elbow joint thickness was identified as a previously unrecognized risk factor associated with CHD. These findings indicated that ML methods accurately predict incident CHD risk. Additionally, ML has identified new incident CHD risk variables.

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Machine-learning Algorithms for Ischemic Heart Disease Prediction: A Systematic Review

Cited by 29 publications

References 28 publications

Big data can help prepare nurses and improve patient outcomes by improving quality, safety, and outcomes

Big data can help prepare nurses and improve patient outcomes by improving quality, safety, and outcomes

Adoption of machine learning systems within the health sector: a systematic review, synthesis and research agenda

Unveiling Coronary Heart Disease Prediction through Machine Learning Techniques: Insights from the Suita Population-Based Cohort Study

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