The objective of this study was to evaluate the outcome, prognostic factors and complications of early stage cervical cancer patients treated with radical hysterectomy and pelvic lymphadenectomy (RHPL). The medical records of cervical cancer patients undergoing RHPL at Chiang Mai University Hospital over 12 years, between January 1995 and December 2006 were reviewed. There were 1,253 patients in the study period. The mean age was 44 years of age. Of these, 26.9% had prior diagnostic conisation. The maximum tumour size was 8 cm. The most common histology was squamous cell carcinoma (67%) followed by adenocarcinoma (23%). The distribution of FIGO staging was: stage IA 8.7%; stage IB 15.8%; stage IB1 61%; stage IB2 6.2%; and stage IIA 8.5%. Pelvic nodes, parametrial and vaginal margin involvement were detected in 15.9%, 10.7% and 3.8% of the patients, respectively. A total of 66.5% of patients underwent RHPL without adjuvant treatment; 12.1% received neoadjuvant chemotherapy. The estimated 10-year recurrence-free survival rate was 90%. Stage IB2/IIA, non-squamous cell carcinoma, nodal involvement and positive vaginal margins were independent, significant, poor prognostic factors. The most common long-term complication was lymphoedema. It was concluded that early stage cervical cancer patients treated with RHPL have long-term favourable outcome with minimal morbidity. Stage IB2 and IIA, non-squamous cell carcinoma, nodal and vaginal involvement were independent adverse prognostic factors.

Gender is an important risk factor in predicting chronic kidney disease (CKD); however, it is under-researched. The purpose of this study was to examine whether gender differences affect the risk factors of early CKD prediction. This study used data from 19,270 adult health screenings, including 5101 with CKD, to screen for 11 independent variables selected as risk factors and to test for the significant effects of statistical Chi-square test variables, using seven machine learning techniques to train the predictive models. Performance indicators included classification accuracy, sensitivity, specificity, and precision. Unbalanced category issues were addressed using three extraction methods: manual sampling, the synthetic minority oversampling technique, and SpreadSubsample. The Chi-square test revealed statistically significant results (p < 0.001) for gender, age, red blood cell count in urine, urine protein (PRO) content, and the PRO-to-urinary creatinine ratio. In terms of classifier prediction performance, the manual extraction method, logistic regression, exhibited the highest average prediction accuracy rate (0.8053) for men, whereas the manual extraction method, linear discriminant analysis, demonstrated the highest average prediction accuracy rate (0.8485) for women. The clinical features of a normal or abnormal PRO-to-urinary creatinine ratio indicated that PRO ratio, age, and urine red blood cell count are the most important risk factors with which to predict CKD in both genders. As a result, this study proposes a prediction model with acceptable prediction accuracy. The model supports doctors in diagnosis and treatment and achieves the goal of early detection and treatment. Based on the evidence-based medicine, machine learning methods are used to develop predictive model in this study. The model has proven to support the prediction of early clinical risk of CKD as much as possible to improve the efficacy and quality of clinical decision making.