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Clinical Model Machine Learning for Gait Observation Cardiovascular Disease Diagnosis
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| Author:
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A.SAMPATH DAKSHINA MURTHY, T.KARTHIKEYAN, B.OMKAR LAKSHMI JAGAN
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| Abstract:
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Heart replacement and nearly 80% of the significant complications or deaths of elderly patients are associated with medical treatment. The potential for predicting mortality and high morbidity in older patients considering cardiac surgery would be improved by conventional risk models if frailty is included in their gait frequency estimation. The current priorities for cardiovascular diagnosis are MRI scans, ultra scans and ECG device examination. Ultrasound imaging is used in this clinical research to classify cardiovascular problems. Specialized methodologies have been used in feature extraction and classification in this segmentation. This research is specifically suited to heart disorders for scientists and physicians. Finally, the performance measurement, i.e. Know, F1, real effectiveness, strong prices. The outputs challenge existing models and improve cardiac diagnostic accuracy.
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| Keyword:
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Gait Observation, Cardiovascular Disease Diagnosis, Machine learning, ultra-scan, middle channel, optical stream, heart diseases classification.
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| EOI:
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-
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| DOI:
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https://doi.org/10.31838/ijpr/2020.12.04.460
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| Download:
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Request For Article
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