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https://hdl.handle.net/10356/44917
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Xie, Vincent JianHan. | - |
dc.date.accessioned | 2011-06-07T02:25:21Z | - |
dc.date.available | 2011-06-07T02:25:21Z | - |
dc.date.copyright | 2011 | en_US |
dc.date.issued | 2011 | - |
dc.identifier.uri | http://hdl.handle.net/10356/44917 | - |
dc.description.abstract | Imbalance data learning is an area of study motivated by application of machine-learning concept on real-world data. Due to the overwhelming instances of majority class, conventional machine learning algorithms have poor performance on prediction for minority class instances. This report explores a method of imbalance data learning, investigating the use of HRV parameters and vital signs as predictor of cardiac arrest occurring 72 hours within hospital admission. This project aims to present a series of steps, starting from the processing of ECG data to the classification of patients using various input features. Input features consist of 16 HRV parameters and 8 vital signs. Several machine learning algorithms were developed and integrated into a main package for the automatic classification of 857 patients. Results show that these algorithms are able to achieve a sensitivity of 64.44% and specificity of 63.79%. This means that there is 64.44% chance of labelling a positive patient as positive, and 63.79% chance of detecting a negative patient as negative. | en_US |
dc.format.extent | 71 p. | en_US |
dc.language.iso | en | en_US |
dc.rights | Nanyang Technological University | - |
dc.subject | DRNTU::Engineering::Electrical and electronic engineering | en_US |
dc.title | Imbalanced data learning for biomedical application | en_US |
dc.type | Final Year Project (FYP) | en_US |
dc.contributor.supervisor | Lin Zhiping | en_US |
dc.contributor.school | School of Electrical and Electronic Engineering | en_US |
dc.description.degree | Bachelor of Engineering | en_US |
item.fulltext | With Fulltext | - |
item.grantfulltext | restricted | - |
Appears in Collections: | EEE Student Reports (FYP/IA/PA/PI) |
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