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Liu Y, Lai J, Hu L, Kang M, Wei S, Lian S, Huang H, Cheng H, Li M, Guan L. Detection of Chylous Plasma Based on Machine Learning and Hyperspectral Techniques. Appl Spectrosc 2024; 78:365-375. [PMID: 38166428 DOI: 10.1177/00037028231214802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2024]
Abstract
Chylous blood is the main cause of unqualified and scrapped blood among volunteer blood donors. Therefore, a diagnostic method that can quickly and accurately identify chylous blood before donation is needed. In this study, the GaiaSorter "Gaia" hyperspectral sorter was used to extract 254 bands of plasma images, ranging from 900 nm to 1700 nm. Four different machine learning algorithms were used, including decision tree, Gaussian Naive Bayes (GaussianNB), perceptron, and stochastic gradient descent models. First, the preliminary classification accuracies were compared with the original data, which showed that the effects of the decision tree and GaussianNB models were better; their average accuracies could reach over 90%. Then, the feature dimension reduction was performed on the original data. The results showed that the effects of the decision tree were better with a classification accuracy of 93.33%. the classification of chylous plasma using different chylous indices suggested that the accuracies of the decision trees model both before and after the feature dimension reductions were the best with over 80% accuracy. The results of feature dimension reduction showed that the characteristic bands corresponded to all kinds of plasma, thereby showing their classification and identification potential. By applying the spectral characteristics of plasma to medical technology, this study suggested a rapid and effective method for the identification of chylous plasma and provided a reference for the blood detection technology to achieve the goal of reducing wasting blood resources and improving the work efficiency of the medical staff.
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Affiliation(s)
- Yafei Liu
- College of Physics and Electronic Information, Gannan Normal University, Ganzhou, Jiangxi, China
| | - Jianxiu Lai
- Central Blood Station of Ganzhou City in Jiangxi Province, Ganzhou, Jiangxi, China
| | - Liying Hu
- Central Blood Station of Ganzhou City in Jiangxi Province, Ganzhou, Jiangxi, China
| | - Meiyan Kang
- Central Blood Station of Ganzhou City in Jiangxi Province, Ganzhou, Jiangxi, China
| | - Siqi Wei
- College of Physics and Electronic Information, Gannan Normal University, Ganzhou, Jiangxi, China
| | - Suyun Lian
- College of Physics and Electronic Information, Gannan Normal University, Ganzhou, Jiangxi, China
| | - Haijun Huang
- College of Physics and Electronic Information, Gannan Normal University, Ganzhou, Jiangxi, China
| | - Hao Cheng
- College of Physics and Electronic Information, Gannan Normal University, Ganzhou, Jiangxi, China
| | - Mengshan Li
- College of Physics and Electronic Information, Gannan Normal University, Ganzhou, Jiangxi, China
| | - Lixin Guan
- College of Physics and Electronic Information, Gannan Normal University, Ganzhou, Jiangxi, China
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Mahmud I, Kabir MM, Mridha MF, Alfarhood S, Safran M, Che D. Cardiac Failure Forecasting Based on Clinical Data Using a Lightweight Machine Learning Metamodel. Diagnostics (Basel) 2023; 13:2540. [PMID: 37568902 PMCID: PMC10417090 DOI: 10.3390/diagnostics13152540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 07/24/2023] [Accepted: 07/28/2023] [Indexed: 08/13/2023] Open
Abstract
Accurate prediction of heart failure can help prevent life-threatening situations. Several factors contribute to the risk of heart failure, including underlying heart diseases such as coronary artery disease or heart attack, diabetes, hypertension, obesity, certain medications, and lifestyle habits such as smoking and excessive alcohol intake. Machine learning approaches to predict and detect heart disease hold significant potential for clinical utility but face several challenges in their development and implementation. This research proposes a machine learning metamodel for predicting a patient's heart failure based on clinical test data. The proposed metamodel was developed based on Random Forest Classifier, Gaussian Naive Bayes, Decision Tree models, and k-Nearest Neighbor as the final estimator. The metamodel is trained and tested utilizing a combined dataset comprising five well-known heart datasets (Statlog Heart, Cleveland, Hungarian, Switzerland, and Long Beach), all sharing 11 standard features. The study shows that the proposed metamodel can predict heart failure more accurately than other machine learning models, with an accuracy of 87%.
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Affiliation(s)
- Istiak Mahmud
- Department of Electrical and Electronic Engineering, Ahsanullah University of Science and Technology, Dhaka 1208, Bangladesh;
| | - Md Mohsin Kabir
- Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka 1216, Bangladesh;
| | - M. F. Mridha
- Department of Computer Science, American International University-Bangladesh, Dhaka 1229, Bangladesh
| | - Sultan Alfarhood
- Department of Computer Science, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi Arabia;
| | - Mejdl Safran
- Department of Computer Science, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi Arabia;
| | - Dunren Che
- School of Computing, Southern Illinois University, Carbondale, IL 62901, USA;
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