Name
Title | ||
|---|---|---|
Risk Assessment of Hypertension in Steel Workers Based on LVQ and Fisher-SVM Deep Excavation. |
Abstract | ||
|---|---|---|
The steel industry is one of the pillar industries in China. The physical and mental health of steel workers is related to the development of China's steel industry. Steel workers have long been working in shifts, high temperatures, noise, highly stressed, and first-line environments. These occupational related factors have an impact on the health of steel workers. At present, the existing hypertension risk scoring models do not include occupational related factors, so they are not applicable to the risk score of hypertension in steel workers. It is necessary to establish a risk scoring model for hypertension in steel workers. In this study, the learning vector quantization (LVQ) neural network algorithm and the Fisher-SVM coupling algorithm are applied to estimate the hypertension risk of steel workers, and the microscopic laws of the "tailing" phenomenon of the two algorithms are analyzed by means of graphics analysis, which can describe the influence trend of sample size change in different intervals on the classification effect. The results show that the classification accuracy of the algorithm depends on the size of the sample space. When the sample size n <= 30 * (k + 1), the Fisher-SVM coupling intelligent algorithm is more applicable. Because its average accuracy rate is 90.00%, the average accuracy of the LVQ algorithm is only 63.34%. When the sample size is n > 30 * (k + 1), the LVQ algorithm is more applicable. Because its average accuracy rate is 93.33%, the average accuracy of the Fisher-SVM coupling intelligent algorithm is only 76.67%. The sample size of this paper is 4422, and the prediction of LVQ neural network model is more accurate. Therefore, based on the relative importance of each risk factor obtained by this model and to establish a steel worker hypertension risk rating scale, the score greater than 18 is considered as the high risk, 12-18 is considered as the medium risk, and less than 12 is considered as the low risk. Through the example's verification, the accuracy rate of the scale is 90.50% and the effect is very good. It shows that the established scoring system can effectively assess the risk of hypertension in steel workers and provide an effective basis for primary prevention of hypertension in steel workers. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ACCESS.2019.2899625 | IEEE ACCESS |
Keywords | Field | DocType |
Steel workers,hypertension,LVQ neural network,Fisher-SVM,risk assessment | Excavation,Computer science,Support vector machine,Learning vector quantization,Risk assessment,Artificial intelligence,Machine learning,Distributed computing | Journal |
Volume | ISSN | Citations |
7 | 2169-3536 | 1 |
PageRank | References | Authors |
0.36 | 0 | 11 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jianhui Wu | 1 | 2 | 1.37 |
| Wei Wei | 2 | 480 | 69.55 |
| Lu Zhang | 3 | 163 | 40.09 |
| Jie Wang | 4 | 1 | 0.69 |
| Robertas Damasevicius | 5 | 281 | 62.75 |
| Jing Li | 6 | 1 | 0.36 |
| Haidong Wang | 7 | 271 | 28.99 |
| Guoli Wang | 8 | 2 | 2.41 |
| Xin Zhang | 9 | 1 | 0.36 |
| Juxiang Yuan | 10 | 1 | 1.71 |
| Marcin Wozniak | 11 | 223 | 38.18 |