The Medical Outcomes Distribution and the Interpretation of Clinical Data Based on C4.5 Algorithm for the RCC Patients in Taiwan
The aim of our study is to explore the medical outcomes among patients in the respiratory care center (RCC) and related factors. A cross-sectional study was performed at a regional hospital in central Taiwan from January 2018 to December 2018. The sample consisted of 236 patients who received RCC me...
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MDPI AG
2021-03-01
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| Online Access: | https://www.mdpi.com/2076-3417/11/6/2566 |
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| author | Hsi-Chieh Lee Ju-Hsia Liu Ching-Sung Ho |
| author_facet | Hsi-Chieh Lee Ju-Hsia Liu Ching-Sung Ho |
| author_sort | Hsi-Chieh Lee |
| collection | DOAJ |
| description | The aim of our study is to explore the medical outcomes among patients in the respiratory care center (RCC) and related factors. A cross-sectional study was performed at a regional hospital in central Taiwan from January 2018 to December 2018. The sample consisted of 236 patients who received RCC medical services. The chi-square test, multiple ordinal logistic regression analyses, and C4.5 decision tree algorithm were performed. The risk factors for medical outcomes in critical or deceased patients were obesity (BMI ≥ 27.0) (OR = 2.426, 95% C.I. = 1.106–5.318, <i>p</i> = 0.027), being imported from home (OR = 2.104, 95% C.I. = 1.106–3.523, <i>p</i> = 0.005), and with the Acute Physiology and Chronic Health Evaluation II (APACHE II) score ≥ 25 (OR = 2.640, 95% C.I. = 1.283–5.433, <i>p</i> = 0.008). The results of the C4.5 algorithm showed a precision of 79.80%, a recall of 78.80%, an F-measure of 78.20%, a receiver operating characteristic curve (ROC) area of 89.20%, and a precision-recall curve (PRC) area of 81.70%. It is important to design effective intervention strategies for patients who are obese and with high APACHE II scores and propose timely treatments for the patients’ onset of disease at home. Moreover, by using the C4.5 algorithm, data can be interpreted in terms of decision trees to aid the understanding of the medical outcomes of the RCC patients. |
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| last_indexed | 2024-03-10T13:16:56Z |
| publishDate | 2021-03-01 |
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| series | Applied Sciences |
| spelling | doaj.art-ed86d2cffe5143b9ba451d0f0c287e962023-11-21T10:19:53ZengMDPI AGApplied Sciences2076-34172021-03-01116256610.3390/app11062566The Medical Outcomes Distribution and the Interpretation of Clinical Data Based on C4.5 Algorithm for the RCC Patients in TaiwanHsi-Chieh Lee0Ju-Hsia Liu1Ching-Sung Ho2Department of Computer Science and Information Engineering, National Quemoy University, 1 University RD, Jinning Township, Kinmen 89250, TaiwanDepartment of Nursing, Jen-Ai Hospital, Taichung 41256, TaiwanDepartment of Long Term Care, National Quemoy University, 1 University RD, Jinning Township, Kinmen 89250, TaiwanThe aim of our study is to explore the medical outcomes among patients in the respiratory care center (RCC) and related factors. A cross-sectional study was performed at a regional hospital in central Taiwan from January 2018 to December 2018. The sample consisted of 236 patients who received RCC medical services. The chi-square test, multiple ordinal logistic regression analyses, and C4.5 decision tree algorithm were performed. The risk factors for medical outcomes in critical or deceased patients were obesity (BMI ≥ 27.0) (OR = 2.426, 95% C.I. = 1.106–5.318, <i>p</i> = 0.027), being imported from home (OR = 2.104, 95% C.I. = 1.106–3.523, <i>p</i> = 0.005), and with the Acute Physiology and Chronic Health Evaluation II (APACHE II) score ≥ 25 (OR = 2.640, 95% C.I. = 1.283–5.433, <i>p</i> = 0.008). The results of the C4.5 algorithm showed a precision of 79.80%, a recall of 78.80%, an F-measure of 78.20%, a receiver operating characteristic curve (ROC) area of 89.20%, and a precision-recall curve (PRC) area of 81.70%. It is important to design effective intervention strategies for patients who are obese and with high APACHE II scores and propose timely treatments for the patients’ onset of disease at home. Moreover, by using the C4.5 algorithm, data can be interpreted in terms of decision trees to aid the understanding of the medical outcomes of the RCC patients.https://www.mdpi.com/2076-3417/11/6/2566respiratory care centermachine learningdata miningC4.5 algorithmBMIAPACHE II scores |
| spellingShingle | Hsi-Chieh Lee Ju-Hsia Liu Ching-Sung Ho The Medical Outcomes Distribution and the Interpretation of Clinical Data Based on C4.5 Algorithm for the RCC Patients in Taiwan Applied Sciences respiratory care center machine learning data mining C4.5 algorithm BMI APACHE II scores |
| title | The Medical Outcomes Distribution and the Interpretation of Clinical Data Based on C4.5 Algorithm for the RCC Patients in Taiwan |
| title_full | The Medical Outcomes Distribution and the Interpretation of Clinical Data Based on C4.5 Algorithm for the RCC Patients in Taiwan |
| title_fullStr | The Medical Outcomes Distribution and the Interpretation of Clinical Data Based on C4.5 Algorithm for the RCC Patients in Taiwan |
| title_full_unstemmed | The Medical Outcomes Distribution and the Interpretation of Clinical Data Based on C4.5 Algorithm for the RCC Patients in Taiwan |
| title_short | The Medical Outcomes Distribution and the Interpretation of Clinical Data Based on C4.5 Algorithm for the RCC Patients in Taiwan |
| title_sort | medical outcomes distribution and the interpretation of clinical data based on c4 5 algorithm for the rcc patients in taiwan |
| topic | respiratory care center machine learning data mining C4.5 algorithm BMI APACHE II scores |
| url | https://www.mdpi.com/2076-3417/11/6/2566 |
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