Classification Algorithm-Based CT Imaging in Diagnosis of Acute Respiratory Distress Syndrome and Analysis of Pathogenic Factors
The study focused on the application value of classification algorithms in processing CT images of acute respiratory distress syndrome (ARDS) and aimed to analyze the pathogenic factors of ARDS. A total of 60 ARDS patients in hospital were selected, and they were divided into ARDS group (38 cases) a...
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| Autores principales: | , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Hindawi Limited
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/df8afc67acab4d7597e8204b469ffdb8 |
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| Sumario: | The study focused on the application value of classification algorithms in processing CT images of acute respiratory distress syndrome (ARDS) and aimed to analyze the pathogenic factors of ARDS. A total of 60 ARDS patients in hospital were selected, and they were divided into ARDS group (38 cases) and non-ARDS group (22 cases) as per diagnostic criteria. There was no significant difference in general data between the two groups (P>0.05). The FWAC algorithm was introduced into CT imaging to classify the image data more accurately. The two groups were compared for the left ventricular ejection fraction (LVEF), oxygenation index PaO2/FiO2 (P/F), Acute Physiology and Chronic Health Evaluation (APACHE II) scores, pH, and PaO2. The results showed that the PaO2, P/F, and APACHE II scores of the two groups were not statistically significant (P>0.05). The P/F of the ARDS group was 136.12, and that of the non-ARDS group was 143.04; the APACHE II score of the ARDS group was 40.1, and that of the non-ARDS group was 62.3, showing no significant difference (P>0.05); the LVEF of the ARDS group was 58.14, and that of the non-ARDS group was 46.26, showing statistically significant differences (P>0.05). When the minimum support was 0.3 and the minimum confidence was 0.5, the value of Recurrence was 0.7082 and the value of Diagnosis was 0.968. The rules generated by the FWAC algorithm can accurately predict the category and were consistent with the expected results. The accuracy of this algorithm was as high as 98.7%, which was significantly higher than that of the conventional CT imaging (88.4%). The rules generated by FWAC were more accurate, assisting doctors in the prevention and diagnosis of ARDS disease. Premature delivery and asphyxia are high-risk factors of ARDS. In conclusion, the FWAC algorithm has a good classification ability of the CT images of ARDS and demonstrates high accuracy. |
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