The Medical Outcomes Distribution and the Interpretation of Clinical Data Based on C4.5 Algorithm for the RCC Patients in Taiwan

Abstract

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, p = 0.027), being imported from home (OR = 2.104, 95% C.I. = 1.106–3.523, p = 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, p = 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.