Raman spectroscopy combined with multiple algorithms for analysis and rapid screening of chronic renal failure
•Raman spectroscopy combined with multivariate statistical methods is proposed to screen chronic renal failure (CRF) patients.•The high diagnostic accuracy of PCA-GS-SVM illustrated the potential of using serum Raman spectra for diagnosing CRF patients.•The results demonstrated great potentials for...
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| Published in: | Photodiagnosis and photodynamic therapy Vol. 30; p. 101792 |
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| Main Authors: | , , , , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
01.06.2020
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| ISSN: | 1572-1000, 1873-1597, 1873-1597 |
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| Abstract | •Raman spectroscopy combined with multivariate statistical methods is proposed to screen chronic renal failure (CRF) patients.•The high diagnostic accuracy of PCA-GS-SVM illustrated the potential of using serum Raman spectra for diagnosing CRF patients.•The results demonstrated great potentials for developing serum Raman spectroscopyinto a novel clinical tool for rapid and non-invasive screening of CRF disease.
Chronic renal failure (CRF) is a symptom of kidney damage in the terminal stages. If a patient is not treated, then CRF will progress to uremia, which greatly reduces the lifespan of the patient. However, current screening strategies, including routine urine tests and medical imaging investigations, have poor sensitivity. Therefore, exploring new and efficient screening methods for CRF such as serum spectroscopy is of great significance. In this study, we first used Raman spectroscopy to classify sera from CRF patients and control subjects. A total of 47 samples from CRF patients and 54 samples from control subjects were acquired. The spectra revealed differences in the phospholipids and proteins between the CRF patients and control subjects. The differences between the CRF patients and control subjects were evaluated by building machine learning models. Subsequent principal component analysis (PCA) was first used for feature extraction. Then, back propagation (BP) neural network, extreme learning machine (ELM), genetic algorithms based on support vector machine (GA-SVM), particle swarm optimization-support vector machine (PSO-SVM), grid search-support vector machine (GS-SVM) and simulated annealing particle swarm optimization based on support vector machine (SAPSO-SVM) algorithms were employed to establish diagnostic models; the diagnostic accuracy of the six classifiers was 70.4 %, 71 %, 83.5 %, 86.9 %, 89.7 % and 82.8 %, respectively, for control subjects and CRF patients. The results show the potential of Raman spectroscopy in differentiating between the control subjects and CRF patients. Based on the limitations of current routine diagnostic methods, serum Raman spectroscopy may be an adjunct/replaceable method for the clinical diagnosis of CRF with the prospective validation of more samples. |
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| AbstractList | •Raman spectroscopy combined with multivariate statistical methods is proposed to screen chronic renal failure (CRF) patients.•The high diagnostic accuracy of PCA-GS-SVM illustrated the potential of using serum Raman spectra for diagnosing CRF patients.•The results demonstrated great potentials for developing serum Raman spectroscopyinto a novel clinical tool for rapid and non-invasive screening of CRF disease.
Chronic renal failure (CRF) is a symptom of kidney damage in the terminal stages. If a patient is not treated, then CRF will progress to uremia, which greatly reduces the lifespan of the patient. However, current screening strategies, including routine urine tests and medical imaging investigations, have poor sensitivity. Therefore, exploring new and efficient screening methods for CRF such as serum spectroscopy is of great significance. In this study, we first used Raman spectroscopy to classify sera from CRF patients and control subjects. A total of 47 samples from CRF patients and 54 samples from control subjects were acquired. The spectra revealed differences in the phospholipids and proteins between the CRF patients and control subjects. The differences between the CRF patients and control subjects were evaluated by building machine learning models. Subsequent principal component analysis (PCA) was first used for feature extraction. Then, back propagation (BP) neural network, extreme learning machine (ELM), genetic algorithms based on support vector machine (GA-SVM), particle swarm optimization-support vector machine (PSO-SVM), grid search-support vector machine (GS-SVM) and simulated annealing particle swarm optimization based on support vector machine (SAPSO-SVM) algorithms were employed to establish diagnostic models; the diagnostic accuracy of the six classifiers was 70.4 %, 71 %, 83.5 %, 86.9 %, 89.7 % and 82.8 %, respectively, for control subjects and CRF patients. The results show the potential of Raman spectroscopy in differentiating between the control subjects and CRF patients. Based on the limitations of current routine diagnostic methods, serum Raman spectroscopy may be an adjunct/replaceable method for the clinical diagnosis of CRF with the prospective validation of more samples. Chronic renal failure (CRF) is a symptom of kidney damage in the terminal stages. If a patient is not treated, then CRF will progress to uremia, which greatly reduces the lifespan of the patient. However, current screening strategies, including routine urine tests and medical imaging investigations, have poor sensitivity. Therefore, exploring new and efficient screening methods for CRF such as serum spectroscopy is of great significance. In this study, we first used Raman spectroscopy to classify sera from CRF patients and control subjects. A total of 47 samples from CRF patients and 54 samples from control subjects were acquired. The spectra revealed differences in the phospholipids and proteins between the CRF patients and control subjects. The differences between the CRF patients and control subjects were evaluated by building machine learning models. Subsequent principal component analysis (PCA) was first used for feature extraction. Then, back propagation (BP) neural network, extreme learning machine (ELM), genetic algorithms based on support vector machine (GA-SVM), particle swarm optimization-support vector machine (PSO-SVM), grid search-support vector machine (GS-SVM) and simulated annealing particle swarm optimization based on support vector machine (SAPSO-SVM) algorithms were employed to establish diagnostic models; the diagnostic accuracy of the six classifiers was 70.4 %, 71 %, 83.5 %, 86.9 %, 89.7 % and 82.8 %, respectively, for control subjects and CRF patients. The results show the potential of Raman spectroscopy in differentiating between the control subjects and CRF patients. Based on the limitations of current routine diagnostic methods, serum Raman spectroscopy may be an adjunct/replaceable method for the clinical diagnosis of CRF with the prospective validation of more samples. Chronic renal failure (CRF) is a symptom of kidney damage in the terminal stages. If a patient is not treated, then CRF will progress to uremia, which greatly reduces the lifespan of the patient. However, current screening strategies, including routine urine tests and medical imaging investigations, have poor sensitivity. Therefore, exploring new and efficient screening methods for CRF such as serum spectroscopy is of great significance. In this study, we first used Raman spectroscopy to classify sera from CRF patients and control subjects. A total of 47 samples from CRF patients and 54 samples from control subjects were acquired. The spectra revealed differences in the phospholipids and proteins between the CRF patients and control subjects. The differences between the CRF patients and control subjects were evaluated by building machine learning models. Subsequent principal component analysis (PCA) was first used for feature extraction. Then, back propagation (BP) neural network, extreme learning machine (ELM), genetic algorithms based on support vector machine (GA-SVM), particle swarm optimization-support vector machine (PSO-SVM), grid search-support vector machine (GS-SVM) and simulated annealing particle swarm optimization based on support vector machine (SAPSO-SVM) algorithms were employed to establish diagnostic models; the diagnostic accuracy of the six classifiers was 70.4 %, 71 %, 83.5 %, 86.9 %, 89.7 % and 82.8 %, respectively, for control subjects and CRF patients. The results show the potential of Raman spectroscopy in differentiating between the control subjects and CRF patients. Based on the limitations of current routine diagnostic methods, serum Raman spectroscopy may be an adjunct/replaceable method for the clinical diagnosis of CRF with the prospective validation of more samples.Chronic renal failure (CRF) is a symptom of kidney damage in the terminal stages. If a patient is not treated, then CRF will progress to uremia, which greatly reduces the lifespan of the patient. However, current screening strategies, including routine urine tests and medical imaging investigations, have poor sensitivity. Therefore, exploring new and efficient screening methods for CRF such as serum spectroscopy is of great significance. In this study, we first used Raman spectroscopy to classify sera from CRF patients and control subjects. A total of 47 samples from CRF patients and 54 samples from control subjects were acquired. The spectra revealed differences in the phospholipids and proteins between the CRF patients and control subjects. The differences between the CRF patients and control subjects were evaluated by building machine learning models. Subsequent principal component analysis (PCA) was first used for feature extraction. Then, back propagation (BP) neural network, extreme learning machine (ELM), genetic algorithms based on support vector machine (GA-SVM), particle swarm optimization-support vector machine (PSO-SVM), grid search-support vector machine (GS-SVM) and simulated annealing particle swarm optimization based on support vector machine (SAPSO-SVM) algorithms were employed to establish diagnostic models; the diagnostic accuracy of the six classifiers was 70.4 %, 71 %, 83.5 %, 86.9 %, 89.7 % and 82.8 %, respectively, for control subjects and CRF patients. The results show the potential of Raman spectroscopy in differentiating between the control subjects and CRF patients. Based on the limitations of current routine diagnostic methods, serum Raman spectroscopy may be an adjunct/replaceable method for the clinical diagnosis of CRF with the prospective validation of more samples. |
| ArticleNumber | 101792 |
| Author | Chen, Chen Chen, Cheng Li, Hongyi Chen, Fangfang Yang, Li Gao, Rui Tang, Jun Lv, XY |
| Author_xml | – sequence: 1 givenname: Cheng surname: Chen fullname: Chen, Cheng organization: College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China – sequence: 2 givenname: Li surname: Yang fullname: Yang, Li organization: The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830000, China – sequence: 3 givenname: Hongyi surname: Li fullname: Li, Hongyi organization: Quality of Products Supervision and Inspection Institute, Urumqi 830011, Xinjiang, China – sequence: 4 givenname: Fangfang surname: Chen fullname: Chen, Fangfang organization: College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China – sequence: 5 givenname: Chen surname: Chen fullname: Chen, Chen organization: College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China – sequence: 6 givenname: Rui surname: Gao fullname: Gao, Rui organization: College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China – sequence: 7 givenname: XY surname: Lv fullname: Lv, XY email: xjuwawj01@163.com organization: College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China – sequence: 8 givenname: Jun surname: Tang fullname: Tang, Jun organization: Physics and Chemistry Detecting Center, Xinjiang University, Urumqi 830046, China |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32353420$$D View this record in MEDLINE/PubMed |
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| Keywords | Support vector machine (SVM) Chronic renal failure (CRF) Raman spectroscopy serum Principal component analysis (PCA) |
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| Snippet | •Raman spectroscopy combined with multivariate statistical methods is proposed to screen chronic renal failure (CRF) patients.•The high diagnostic accuracy of... Chronic renal failure (CRF) is a symptom of kidney damage in the terminal stages. If a patient is not treated, then CRF will progress to uremia, which greatly... |
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| SubjectTerms | Algorithms Chronic renal failure (CRF) Hematologic Tests - methods Humans Kidney Failure, Chronic - diagnosis Machine Learning Neural Networks, Computer Principal Component Analysis Principal component analysis (PCA) Raman spectroscopy Sensitivity and Specificity serum Spectrum Analysis, Raman - methods Support vector machine (SVM) |
| Title | Raman spectroscopy combined with multiple algorithms for analysis and rapid screening of chronic renal failure |
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