FPGA-based urinalysis for urinary tract infection detection using principal component analysis
Urinalysis is considered to be a common test performed in laboratory in order to diagnose Urinary Tract Infection (UTI). It undergoes three stages, which include macroscopic, dipstick, and microscopic analysis. This paper discusses about a new way of performing urinalysis for UTI detection through a...
Saved in:
Main Authors: | , , , |
---|---|
Format: | text |
Language: | English |
Published: |
Animo Repository
2016
|
Subjects: | |
Online Access: | https://animorepository.dlsu.edu.ph/etd_bachelors/8595 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | De La Salle University |
Language: | English |
id |
oai:animorepository.dlsu.edu.ph:etd_bachelors-9240 |
---|---|
record_format |
eprints |
spelling |
oai:animorepository.dlsu.edu.ph:etd_bachelors-92402021-08-24T06:50:48Z FPGA-based urinalysis for urinary tract infection detection using principal component analysis Cualquiera, James Kenneth P. Loriaga, Karl Joszep A. Roxas, Paulo Gabriel N. Ybanez, Kristine D. V. Urinalysis is considered to be a common test performed in laboratory in order to diagnose Urinary Tract Infection (UTI). It undergoes three stages, which include macroscopic, dipstick, and microscopic analysis. This paper discusses about a new way of performing urinalysis for UTI detection through a Field Programmable Gate Array (FPGA) and with the use of five different sensors that measure five different components specifically sodium, nitrate, potassium, and pH level of a urine sample. The designed system has an accuracy of 94.13% for the urinalysis. To be able to detect the presence of UTI in urines, an outlier detection method, Principal Component Analysis (PCA), was used. PCA is a tool used in reducing multidimensional data to lesser dimensions while keeping all the information. The selection of the parameters to be measured is important in order to increase the accuracy of detection. Because of this, the group compared the accuracy of UTI detection when the pH sensor was used and if it was removed. The accuracy of the designed system for UTI detection increased to 83.33% when pH sensor is removed. This paper also discusses about the implementation of PCA on an FPGA. The computed principal component by the FPGA was compared to be computed principal components by MATLAB and has an accuracy of 99.917%. 2016-01-01T08:00:00Z text https://animorepository.dlsu.edu.ph/etd_bachelors/8595 Bachelor's Theses English Animo Repository Urine--Analysis Urinary tract infections Field programmable gate arrays Principal components analysis |
institution |
De La Salle University |
building |
De La Salle University Library |
continent |
Asia |
country |
Philippines Philippines |
content_provider |
De La Salle University Library |
collection |
DLSU Institutional Repository |
language |
English |
topic |
Urine--Analysis Urinary tract infections Field programmable gate arrays Principal components analysis |
spellingShingle |
Urine--Analysis Urinary tract infections Field programmable gate arrays Principal components analysis Cualquiera, James Kenneth P. Loriaga, Karl Joszep A. Roxas, Paulo Gabriel N. Ybanez, Kristine D. V. FPGA-based urinalysis for urinary tract infection detection using principal component analysis |
description |
Urinalysis is considered to be a common test performed in laboratory in order to diagnose Urinary Tract Infection (UTI). It undergoes three stages, which include macroscopic, dipstick, and microscopic analysis. This paper discusses about a new way of performing urinalysis for UTI detection through a Field Programmable Gate Array (FPGA) and with the use of five different sensors that measure five different components specifically sodium, nitrate, potassium, and pH level of a urine sample. The designed system has an accuracy of 94.13% for the urinalysis. To be able to detect the presence of UTI in urines, an outlier detection method, Principal Component Analysis (PCA), was used. PCA is a tool used in reducing multidimensional data to lesser dimensions while keeping all the information. The selection of the parameters to be measured is important in order to increase the accuracy of detection. Because of this, the group compared the accuracy of UTI detection when the pH sensor was used and if it was removed. The accuracy of the designed system for UTI detection increased to 83.33% when pH sensor is removed. This paper also discusses about the implementation of PCA on an FPGA. The computed principal component by the FPGA was compared to be computed principal components by MATLAB and has an accuracy of 99.917%. |
format |
text |
author |
Cualquiera, James Kenneth P. Loriaga, Karl Joszep A. Roxas, Paulo Gabriel N. Ybanez, Kristine D. V. |
author_facet |
Cualquiera, James Kenneth P. Loriaga, Karl Joszep A. Roxas, Paulo Gabriel N. Ybanez, Kristine D. V. |
author_sort |
Cualquiera, James Kenneth P. |
title |
FPGA-based urinalysis for urinary tract infection detection using principal component analysis |
title_short |
FPGA-based urinalysis for urinary tract infection detection using principal component analysis |
title_full |
FPGA-based urinalysis for urinary tract infection detection using principal component analysis |
title_fullStr |
FPGA-based urinalysis for urinary tract infection detection using principal component analysis |
title_full_unstemmed |
FPGA-based urinalysis for urinary tract infection detection using principal component analysis |
title_sort |
fpga-based urinalysis for urinary tract infection detection using principal component analysis |
publisher |
Animo Repository |
publishDate |
2016 |
url |
https://animorepository.dlsu.edu.ph/etd_bachelors/8595 |
_version_ |
1772834843802992640 |