Assessment of the Performances of Adaptive Neuro-Fuzzy Inference System and Two Statistical Methods for Diagnosing Coronary Artery Disease.

Background: The accurate diagnosis of cardiac disease is vital in managing patients' health. Data mining and machine learning techniques play an important role in the diagnosis of heart disease. We aimed to examine the diagnostic performances of an adaptive neuro-fuzzy inference system (ANFIS) for predicting coronary artery disease and compare this with two statistical methods: flexible discriminant analysis (FDA) and logistic regression (LR).

Early Detection of Gestational Trophoblastic Neoplasia Based on Serial Measurement of Human Chorionic Gonadotrophin Hormone in Women with Molar Pregnancy.

Background: The majority of studies which investigate the predicted power of Human chorionic gonadotropin (hCG) levels to the occurrence of Gestational trophoblastic neoplasia (GTN) considered the effect of a single measurement of hCG or used classical statistical methods without considering the endogenous marker. The aim of this study is to investigate the association between weekly measurements of β-hCG with time to GTN occurring, using a robust Bayesian joint modeling.

Methods: Data of 201 women with a molar pregnancy were considered for this retrospective cohort study.

Data on trend changes of drinking groundwater resources quality in Sabzevar city (A case study).

The data of this study was conducted to evaluate the groundwater quality trend changes in Sabzevar (Iran, Khorasan Razavi) during one decade (2006-2016). The Mean ± SD of electrical conductivity (as µmhos/cm), total hardness (as calcium carbonate, mg/l) and total dissolved solid in the first and end year of the study were 605.45 ± 194.