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- W4323021737 abstract "In Nigeria, prose format is used to present and perform analysis on chest x-ray reports and this often results in delayed response from the clinicians. Therefore, with a view to developing a system for analyzing chest x-ray reports for diagnosing cardiomegaly, linear support vector machine algorithm was utilized to formulate an adaptable model with a train-test split of 70:30 for six hundred and fifty (650) de-identified patients' information. Attributes relevant to cardiomegaly from the collected dataset were extracted using Term frequency/inverse document frequency technique. This work provides an adequate requirement for diagnosis design with accuracy of 93.69%. Its implementation in software application has the potential to reduce delay in attending to patients and can also help the clinicians focus on the findings from chest x-ray reports." @default.
- W4323021737 created "2023-03-04" @default.
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- W4323021737 date "2022-11-01" @default.
- W4323021737 modified "2023-10-16" @default.
- W4323021737 title "An Adaptable SVM Model for Abnormalities Detection in Chest X-ray Reports" @default.
- W4323021737 cites W114364920 @default.
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- W4323021737 doi "https://doi.org/10.1109/ited56637.2022.10051548" @default.
- W4323021737 hasPublicationYear "2022" @default.
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