Towards Early Diagnosis and Intervention: An Ensemble Voting Model for Precise Vital Sign Prediction in Respiratory Disease
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- @Article{Ahmed:JBHI,
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author = "Usman Ahmed and Jerry Chun-Wei Lin and
Gautam Srivastava",
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journal = "IEEE Journal of Biomedical and Health Informatics",
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title = "Towards Early Diagnosis and Intervention: An Ensemble
Voting Model for Precise Vital Sign Prediction in
Respiratory Disease",
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year = "2023",
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abstract = "Worldwide, cardiovascular and chronic respiratory
diseases account for approximately 19 million deaths
annually. Evidence indicates that the ongoing COVID-19
pandemic directly contributes to increased blood
pressure, cholesterol, as well as blood glucose levels.
Timely screening of critical physiological vital signs
benefits both healthcare providers and individuals by
detecting potential health issues. This study aims to
implement a machine learning-based prediction and
classification system to forecast vital signs
associated with cardiovascular and chronic respiratory
diseases. The system predicts patients' health status
and notifies caregivers and medical professionals when
necessary. Using real-world data, a linear regression
model inspired by the Facebook Prophet model was
developed to predict vital signs for the upcoming 180
seconds. With 180 seconds of lead time, caregivers can
potentially save patients' lives through early
diagnosis of their health conditions. For this purpose,
a Naive Bayes classification model, a Support Vector
Machine model, a Random Forest model, and genetic
programming-based hyper tunning were employed. The
proposed model outdoes previous attempts at vital sign
prediction. Compared with alternative methods, the
Facebook Prophet model has the best mean square in
predicting vital signs. A hyperparameter-tuning is used
to refine the model, yielding improved short- and
long-term outcomes for each and every vital sign.
Furthermore, the F-measure for the proposed
classification model is 0.98 with an increase of 0.21.
The incorporation of additional elements, such as
momentum indicators, could increase the model's
flexibility with calibration. The findings of this
study demonstrate that the proposed model is more
accurate in predicting vital signs and trends.",
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keywords = "genetic algorithms, genetic programming, Diseases,
Medical diagnostic imaging, Medical services, Heart,
Predictive models, Machine learning, Decision trees,
Artificial intelligence, Sensor readings, Heart
disease, Long-term lung disease",
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DOI = "doi:10.1109/JBHI.2023.3270888",
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ISSN = "2168-2208",
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notes = "Also known as \cite{10121013}",
- }
Genetic Programming entries for
Usman Ahmed
Jerry Chun-Wei Lin
Gautam Srivastava
Citations