Automating and improving cardiovascular disease prediction using Machine learning and EMR data features from a regional healthcare system

Li, Qi; Câmpan, Alina; Ren, Ai; Eid, Wael

doi:10.1016/j.ijmedinf.2022.104786

Cited by 19 publications

(10 citation statements)

References 42 publications

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“…Expanding these techniques to a larger dataset with more potential risk factors may increase the effectiveness of these techniques. Qi et al found that machine learning techniques applied to electronic medical records (EMR) produced the best prediction models (AUC 0.902) when the EMR data contained both longitudinal and cross-sectional patient data [27]. Distinct types of CVD may require different modeling approaches.…”

Section: Discussionmentioning

confidence: 99%

Predicting cardiovascular disease using different blood pressure guidelines

Bopp

Briggs

Orlando

et al. 2023

IJPHS

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The criteria used to categorize patients as either hypertensive or normotensive were changed in 2017 by the American Heart Association and the American College of Cardiology (AHA/ACC). The updated guidelines lowered the criteria by which individuals are classified as hypertensive; systolic blood pressure (SBP) cut-off from ≥140 mmHg to ≥130 mmHg and diastolic blood pressure from ≥90 mmHg to ≥80 mmHg. The purpose of this study was to investigate what effect these changes in diagnostic criteria had on the ability of supervised learning to predict cardiovascular disease. Three models were developed and tested. Two models using a binned hypertension measure based on either the AHA/ACC new released guidelines or the Joint National Committee on the Prevention, Detection, Evaluation, and Treatment of High Blood Pressure (JNC7) original guidelines. The third model used SBP as a continuous variable. Data from 68,657 patients was processed through decision tree algorithm to determine which model offered the best accuracy. For both female and male subjects, the model with SBP returned the best area under the receiver operator characteristic curve and overall better sensitivity and specificity values. Our results showed that changing the criteria by which individuals are classified as hypertensive or normotensive negatively impacted the ability of decision tree to predict cardiovascular disease in both females and males.

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Section: Discussionmentioning

confidence: 99%

Predicting cardiovascular disease using different blood pressure guidelines

Bopp

Briggs

Orlando

et al. 2023

IJPHS

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“…In healthcare, there is a massive quantity of data accessible. This pertains to electronic medical records (EMRs), which may include all forms of data [ 120 ]. For instance, structured data refers to data that are simple to categorize in a database; they might contain a set of features and records such as patient’s biodata and generic health complaints such as fever or nausea [ 121 ].…”

Section: Artificial Intelligencementioning

confidence: 99%

Recent Advancements in Emerging Technologies for Healthcare Management Systems: A Survey

et al. 2022

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In recent times, the growth of the Internet of Things (IoT), artificial intelligence (AI), and Blockchain technologies have quickly gained pace as a new study niche in numerous collegiate and industrial sectors, notably in the healthcare sector. Recent advancements in healthcare delivery have given many patients access to advanced personalized healthcare, which has improved their well-being. The subsequent phase in healthcare is to seamlessly consolidate these emerging technologies such as IoT-assisted wearable sensor devices, AI, and Blockchain collectively. Surprisingly, owing to the rapid use of smart wearable sensors, IoT and AI-enabled technology are shifting healthcare from a conventional hub-based system to a more personalized healthcare management system (HMS). However, implementing smart sensors, advanced IoT, AI, and Blockchain technologies synchronously in HMS remains a significant challenge. Prominent and reoccurring issues such as scarcity of cost-effective and accurate smart medical sensors, unstandardized IoT system architectures, heterogeneity of connected wearable devices, the multidimensionality of data generated, and high demand for interoperability are vivid problems affecting the advancement of HMS. Hence, this survey paper presents a detailed evaluation of the application of these emerging technologies (Smart Sensor, IoT, AI, Blockchain) in HMS to better understand the progress thus far. Specifically, current studies and findings on the deployment of these emerging technologies in healthcare are investigated, as well as key enabling factors, noteworthy use cases, and successful deployments. This survey also examined essential issues that are frequently encountered by IoT-assisted wearable sensor systems, AI, and Blockchain, as well as the critical concerns that must be addressed to enhance the application of these emerging technologies in the HMS.

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“…According to the most recent estimations ( 16 , 17 ), CVD will be responsible for the deaths of about 23 million people by the year 2030. Infarction of the myocardium, atrial fibrillation, and heart failure are all instances of different types of CVD ( 18 , 19 ). The incidence of cardiovascular disease can be influenced by a number of factors, including racial or ethnic background, age, gender, body mass index (BMI), height, and length of torso, as well as the outcomes of blood tests that evaluate factors such as renal function, liver function, and cholesterol levels ( 20 , 21 ) which is shown in Figure 1 .…”

Section: Introductionmentioning

confidence: 99%

Cardiovascular diseases prediction by machine learning incorporation with deep learning

Subramani¹,

Varshney

Anand

et al. 2023

Front. Med.

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It is yet unknown what causes cardiovascular disease (CVD), but we do know that it is associated with a high risk of death, as well as severe morbidity and disability. There is an urgent need for AI-based technologies that are able to promptly and reliably predict the future outcomes of individuals who have cardiovascular disease. The Internet of Things (IoT) is serving as a driving force behind the development of CVD prediction. In order to analyse and make predictions based on the data that IoT devices receive, machine learning (ML) is used. Traditional machine learning algorithms are unable to take differences in the data into account and have a low level of accuracy in their model predictions. This research presents a collection of machine learning models that can be used to address this problem. These models take into account the data observation mechanisms and training procedures of a number of different algorithms. In order to verify the efficacy of our strategy, we combined the Heart Dataset with other classification models. The proposed method provides nearly 96 percent of accuracy result than other existing methods and the complete analysis over several metrics has been analysed and provided. Research in the field of deep learning will benefit from additional data from a large number of medical institutions, which may be used for the development of artificial neural network structures.

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Automating and improving cardiovascular disease prediction using Machine learning and EMR data features from a regional healthcare system

Cited by 19 publications

References 42 publications

Predicting cardiovascular disease using different blood pressure guidelines

Predicting cardiovascular disease using different blood pressure guidelines

Recent Advancements in Emerging Technologies for Healthcare Management Systems: A Survey

Cardiovascular diseases prediction by machine learning incorporation with deep learning

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