A Machine-Learning-Based System for Prediction of Cardiovascular and Chronic Respiratory Diseases
Open accessAbstract
Cardiovascular and chronic respiratory diseases are global threats to public health and cause approximately 19 million deaths worldwide annually. This high mortality rate can be reduced with the use of technological advancements in medical science that can facilitate continuous monitoring of physiological parameters-blood pressure, cholesterol levels, blood glucose, etc. The futuristic values of these critical physiological or vital sign parameters not only enable in-time assistance from medical experts and caregivers but also help patients manage their health status by receiving relevant regular alerts/advice from healthcare practitioners. In this study, we propose a machine-learning-based prediction and classification system to determine futuristic values of related vital signs for both cardiovascular and chronic respiratory diseases. Based on the prediction of futuristic values, the proposed system can classify patients' health status to alarm the caregivers and medical experts. In this machine-learning-based prediction and classification model, we have used a real vital sign dataset. To predict the next 1-3 minutes of vital sign values, several regression techniques (i.e., linear regression and polynomial regression of degrees 2, 3, and 4) have been tested. For caregivers, a 60-second prediction and to facilitate emergency medical assistance, a 3-minute prediction of vital signs is used. Based on the predicted vital signs values, the patient's overall health is assessed using three machine learning classifiers, i.e., Support Vector Machine (SVM), Naive Bayes, and Decision Tree. Our results show that the Decision Tree can correctly classify a patient's health status based on abnormal vital sign values and is helpful in timely medical care to the patients.
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AI algorithms process data from wearables, electronic health records, and other sources to reveal deviations in heart rate and activity metrics that indicate onset of cardiovascular disease.
“AI algorithms process data derived from wearables, electronic health records, and other sources to reveal deviations in a person's heart rate and activity metrics [53].”
From Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions (2023) · cites this paper as [53]
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- Authors
- Shah, W., Aleem, M., Iqbal, M. A., Islam, M. A., Ahmed, U., Srivastava, G., & Lin, J. C.-W.
- Affiliations
- Capital University of Science and Technology (PK)
- National University of Computer and Emerging Sciences (PK)
- Southwest Jiaotong University (CN)
- Western Norway University of Applied Sciences (NO)
- China Medical University (TW)
- Corresponding author
- Lin, Jerry Chun-Wei
- ORCID
- Venue
- Journal of Healthcare Engineering
- Publisher
- Wiley
- Published
- 2021-11-01
- Volume
- 2021
- Pages
- 1-17
- ISSN
- 2040-2295 (ISSN-L)
- 2040-2309
- PMC
- PMC8575608
- OpenAlex
- W3210290409
- Cited by
- 18
- References
- 34
- Citations by year
- 2025: 2 · 2024: 1 · 2023: 8 · 2022: 7
- FWCI
- 3.49
- Type
- journal article
- Language
- en
- Status
- Retracted
- Update notices
- retraction (2023-09-20)
- Open access
- hybrid
- doi.org
- Locations
- Journal of Healthcare Engineering (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- NORA - Norwegian Open Research Archives (submittedVersion, cc-by)
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Keywords
- cardiovascular disease, chronic respiratory disease, health status classification, early warning alerts, blood pressure, blood glucose, cholesterol levels, decision tree classification, support vector machine, naive Bayes, polynomial regression
- MeSH terms
- Machine Learning, Algorithms, Bayes Theorem, Humans, Vital Signs, Support Vector Machine
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