Exploratory Study on Screening Chronic Renal Failure Based on Fourier Transform Infrared Spectroscopy and a Support Vector Machine Algorithm
Chronic renal failure (CRF) is a clinically serious kidney disease. If the patient is not treated in a timely manner, CRF will develop into uremia. However, current diagnostic methods, such as routine blood examinations and medical imaging, have low sensitivity. Therefore, it is important to explore...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
|
| Series: | Journal of Spectroscopy |
| Online Access: | http://dx.doi.org/10.1155/2020/7379242 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850234883207593984 |
|---|---|
| author | Yushuai Yuan Li Yang Rui Gao Cheng Chen Min Li Jun Tang Xiaoyi Lv Ziwei Yan |
| author_facet | Yushuai Yuan Li Yang Rui Gao Cheng Chen Min Li Jun Tang Xiaoyi Lv Ziwei Yan |
| author_sort | Yushuai Yuan |
| collection | DOAJ |
| description | Chronic renal failure (CRF) is a clinically serious kidney disease. If the patient is not treated in a timely manner, CRF will develop into uremia. However, current diagnostic methods, such as routine blood examinations and medical imaging, have low sensitivity. Therefore, it is important to explore new and effective diagnostic methods for CRF, such as serum spectroscopy. This study proposes a cost-effective and reliable method for detecting CRF based on Fourier transform infrared (FT-IR) spectroscopy and a support vector machine (SVM) algorithm. We measured and analyzed the FT-IR spectra of serum from 44 patients with CRF and 54 individuals with normal renal function. The partial least squares (PLS) algorithm was applied to reduce the dimensionality of the high-dimensional spectral data. The samples were input into the SVM after division by the Kennard–Stone (KS) algorithm. Compared with other models, the SVM optimized by a grid search (GS) algorithm performed the best. The sensitivity of our diagnostic model was 93.75%, the specificity was 100%, and the accuracy was 96.97%. The results demonstrate that FT-IR spectroscopy combined with a pattern recognition algorithm has great potential in screening patients with CRF. |
| format | Article |
| id | doaj-art-17929cd353f24693b232fa63ccdf0fda |
| institution | OA Journals |
| issn | 2314-4920 2314-4939 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Spectroscopy |
| spelling | doaj-art-17929cd353f24693b232fa63ccdf0fda2025-08-20T02:02:29ZengWileyJournal of Spectroscopy2314-49202314-49392020-01-01202010.1155/2020/73792427379242Exploratory Study on Screening Chronic Renal Failure Based on Fourier Transform Infrared Spectroscopy and a Support Vector Machine AlgorithmYushuai Yuan0Li Yang1Rui Gao2Cheng Chen3Min Li4Jun Tang5Xiaoyi Lv6Ziwei Yan7College of Information Science and Engineering, Xinjiang University, Urumqi 830046, ChinaThe First Affiliated Hospital of Xinjiang Medical University, Urumqi 830000, ChinaCollege of Information Science and Engineering, Xinjiang University, Urumqi 830046, ChinaCollege of Information Science and Engineering, Xinjiang University, Urumqi 830046, ChinaCollege of Software, Xinjiang University, Urumqi 830046, ChinaPhysics and Chemistry Detecting Center, Xinjiang University, Urumqi 830046, ChinaCollege of Information Science and Engineering, Xinjiang University, Urumqi 830046, ChinaCollege of Information Science and Engineering, Xinjiang University, Urumqi 830046, ChinaChronic renal failure (CRF) is a clinically serious kidney disease. If the patient is not treated in a timely manner, CRF will develop into uremia. However, current diagnostic methods, such as routine blood examinations and medical imaging, have low sensitivity. Therefore, it is important to explore new and effective diagnostic methods for CRF, such as serum spectroscopy. This study proposes a cost-effective and reliable method for detecting CRF based on Fourier transform infrared (FT-IR) spectroscopy and a support vector machine (SVM) algorithm. We measured and analyzed the FT-IR spectra of serum from 44 patients with CRF and 54 individuals with normal renal function. The partial least squares (PLS) algorithm was applied to reduce the dimensionality of the high-dimensional spectral data. The samples were input into the SVM after division by the Kennard–Stone (KS) algorithm. Compared with other models, the SVM optimized by a grid search (GS) algorithm performed the best. The sensitivity of our diagnostic model was 93.75%, the specificity was 100%, and the accuracy was 96.97%. The results demonstrate that FT-IR spectroscopy combined with a pattern recognition algorithm has great potential in screening patients with CRF.http://dx.doi.org/10.1155/2020/7379242 |
| spellingShingle | Yushuai Yuan Li Yang Rui Gao Cheng Chen Min Li Jun Tang Xiaoyi Lv Ziwei Yan Exploratory Study on Screening Chronic Renal Failure Based on Fourier Transform Infrared Spectroscopy and a Support Vector Machine Algorithm Journal of Spectroscopy |
| title | Exploratory Study on Screening Chronic Renal Failure Based on Fourier Transform Infrared Spectroscopy and a Support Vector Machine Algorithm |
| title_full | Exploratory Study on Screening Chronic Renal Failure Based on Fourier Transform Infrared Spectroscopy and a Support Vector Machine Algorithm |
| title_fullStr | Exploratory Study on Screening Chronic Renal Failure Based on Fourier Transform Infrared Spectroscopy and a Support Vector Machine Algorithm |
| title_full_unstemmed | Exploratory Study on Screening Chronic Renal Failure Based on Fourier Transform Infrared Spectroscopy and a Support Vector Machine Algorithm |
| title_short | Exploratory Study on Screening Chronic Renal Failure Based on Fourier Transform Infrared Spectroscopy and a Support Vector Machine Algorithm |
| title_sort | exploratory study on screening chronic renal failure based on fourier transform infrared spectroscopy and a support vector machine algorithm |
| url | http://dx.doi.org/10.1155/2020/7379242 |
| work_keys_str_mv | AT yushuaiyuan exploratorystudyonscreeningchronicrenalfailurebasedonfouriertransforminfraredspectroscopyandasupportvectormachinealgorithm AT liyang exploratorystudyonscreeningchronicrenalfailurebasedonfouriertransforminfraredspectroscopyandasupportvectormachinealgorithm AT ruigao exploratorystudyonscreeningchronicrenalfailurebasedonfouriertransforminfraredspectroscopyandasupportvectormachinealgorithm AT chengchen exploratorystudyonscreeningchronicrenalfailurebasedonfouriertransforminfraredspectroscopyandasupportvectormachinealgorithm AT minli exploratorystudyonscreeningchronicrenalfailurebasedonfouriertransforminfraredspectroscopyandasupportvectormachinealgorithm AT juntang exploratorystudyonscreeningchronicrenalfailurebasedonfouriertransforminfraredspectroscopyandasupportvectormachinealgorithm AT xiaoyilv exploratorystudyonscreeningchronicrenalfailurebasedonfouriertransforminfraredspectroscopyandasupportvectormachinealgorithm AT ziweiyan exploratorystudyonscreeningchronicrenalfailurebasedonfouriertransforminfraredspectroscopyandasupportvectormachinealgorithm |