The consumer price index prediction using machine learning approaches: Evidence from the United States.

The consumer price index (CPI) is one of the most important macroeconomic indicators for determining inflation, and accurate predictions of CPI changes are important for a country's economic development. This study uses multivariate linear regression (MLR), support vector regression (SVR), autoregressive distributed lag (ARDL), and multivariate adaptive regression splines (MARS) to predict the CPI of the United States. Data from January 2017 to February 2022 were randomly selected and divided into two stages: 80 % for training and 20% for testing. The US CPI was modeled for the observed period and relied on a mix of elements, including crude oil price, world gold price, and federal fund effective rate. Evaluation metrics-mean absolute percentage value, mean absolute error, root mean square error, R-squared, and correlation of determination-were employed to estimate forecasted values. The MLR, SVR, ARDL, and MARS models attained high accuracy parameters, while the MARS algorithm generated higher accuracy in US CPI forecasts than the others in the testing phase. These outputs could support the US government in overseeing economic policies, sectors, and social security, thereby boosting national economic development.

Prognostic Factors of Radiofrequency Ablation plus Systemic Chemotherapy for Unresectable Colorectal Cancer with Liver Metastasis.

INTRODUCTION: Survival outcomes in patients with unresectable colorectal cancer (CRC) liver metastasis treated by radiofrequency ablation (RFA) combined with systemic chemotherapy and correlation with potential prognostic factors were investigated. Material and Methods. A retrospective cohort study was conducted on 61 CRC patients with unresectable liver metastasis who underwent liver tumor-directed percutaneous RFA combined with conventional systemic chemotherapy between October 2013 and September 2018. Survival analyses were conducted using the Kaplan-Meier method, and the log-rank test was used to characterize differences in the median survival time and the 1-year, 3-year, and 5-year overall survival rates of subgroups to identify prognostic factors. RESULTS: Median overall survival and progression-free survival of all patients were 32 and 14 months, respectively. The cumulative survival rates at 1-, 3-, and 5-years were 93.2%; 44.5%, and 38.2%, respectively. Univariate analysis revealed that pre-RFA serum CEA levels, Eastern Cooperative Oncology Group (ECOG) status, number of liver lesions, the size of the largest lesion, and the total lesion size were prognostic factors. However, multivariate analysis demonstrated that only the number of liver lesions and the size of the largest lesion were independent prognostic factors for survival. CONCLUSION: RFA plus systemic chemotherapy provides an encouraging survival outcome for patients with unresectable CRC liver metastasis. Multivariate analysis demonstrated that the number and size of liver metastatic lesions are independent prognostic factors for survival.