AbstractResearch has failed to resolve the dilemma experienced by localized prostate cancer patients who must choose between radical prostatectomy (RP) and external beam radiotherapy (RT). Because the Charlson Comorbidity Index (CCI) is a measurable factor that affects survival events, this research seeks to validate the potential of the CCI to improve the accuracy of various prediction models. Thus, we employed the Cox proportional hazard model and machine learning methods, including random forest (RF) and support vector machine (SVM), to model the data of medical records in the National Health Insurance Research Database (NHIRD). In total, 8581 individuals were enrolled, of whom 4879 had received RP and 3702 had received RT. Patients in the RT group were older and exhibited higher CCI scores and higher incidences of some CCI items. Moderate-to-severe liver disease, dementia, congestive heart failure, chronic pulmonary disease, and cerebrovascular disease all increase the risk of overall death in the Cox hazard model. The CCI-reinforced SVM and RF models are 85.18% and 81.76% accurate, respectively, whereas the SVM and RF models without the use of the CCI are relatively less accurate, at 75.81% and 74.83%, respectively. Therefore, CCI and some of its items are useful predictors of overall and prostate-cancer-specific survival and could constitute valuable features for machine-learning modeling.

Objectives: This study aimed to validate the application of the 3 × 2 achievement goal model in sports. Motivations: In order to offer new perspectives on achievement goals, this study explores 3 × 2 achievement goals used in competitive sports, and the prediction of passion and psychological well-being for sports. Methods: The study sample consists of 406 college and university athletes, including 230 males and 176 females. Average age of the subjects was 20.34 years. Average length of years of sports participation was 8.23 years. Data were collected with a questionnaire that incorporated a 3 × 2 achievement goal scale, a sports passion scale, and the Psychological Well-Being Scale. Statistical Methods: Data were analyzed using descriptive statistics for fuzzy data, fuzzy correlation coefficients, and fuzzy regression models. Finding: 1. There was a correlation between every two of task-approach, task-avoidance, self-approach, self-avoidance, other-approach, other-avoidance, harmonious passion, obsessive passion, and psychological well-being. 2. Among college and university athletes, task-approach and self-approach positively influence harmonious passion; task-approach, self-approach, other-approach, and other-avoidance positively influence obsessive passion; task-avoidance negatively influences obsessive passion; task-approach and self-approach positively influence psychological well-being, and task-avoidance negatively influences psychological well-being. Innovations: Use of the 3 × 2 achievement goal scale is applicable to college sportsmen in Taiwan, and the research method uses fuzzy statistical analysis, which breaks through the barriers of traditional psychological survey methods, and will improve the research quality of the sample survey. This study provides new techniques for research on psychological trends in sports. Value: In the future, coaches and athletes should focus on task-approach and self-approach goals in order to enhance the college or university athletes’ harmonious passion for a positive impact on their psychological well-being when they engage in sports through their own free will.