RESUMO
BACKGROUND: Metabolic syndrome (MetS) is a cluster metabolic disorder that includes central obesity, insulin resistance, hypertension, and dyslipidemia, and is highly associated with an increased risk of developing non-communicable diseases (NCDs). This study aimed to compare the reliability of anthro-metabolic indices [visceral adiposity index (VAI), body roundness index (BRI), and a body shape index (BSI), body adiposity index (BAI), lipid accumulation product (LAP), waist to hip ratio, and waist to height ratio] in predicting MetS in Iranian older people. METHODS: This cross-sectional study was conducted based on the data of 2426 adults aged ≥60 years that participated in the second stage of the Bushehr Elderly Health (BEH) program, a population-based prospective cohort study being conducted in Bushehr, Iran. MetS was defined based on the revised National Cholesterol Education Program Adult Treatment Panel III (NCEP-ATP III) criteria. The receiver operating characteristic (ROC) curve analysis was used to assess predictive performance of anthro-metabolic indices and determine optimal cutoff values. Logistic regression analysis was applied to determine the associations between MetS and indices. RESULTS: 2426 subjects (48.1% men) with mean ± SD age of 69.34 ± 6.40 years were included in the study. According to ATP III criteria, 34.8% of men and 65.2% of women had MetS (P < 0.001). Of the seven examined indices, the AUCs of VAI and LAP in both genders were higher than AUCs of other anthro-metabolic indices. Also, in general population, VAI and LAP had the greatest predictive power for MetS with AUC 0.87(0.86-0.89) and 0.87(0.85-0.88), respectively. The lowest AUC in total population belonged to BSI with the area under the curve of 0.60(0.58-0.62). After adjusting for potential confounders (e.g. age, sex, education, physical activity, current smoking) in the logistic regression model, the highest OR in the total population was observed for VAI and LAP, which was 16.63 (13.31-20.79) and 12.56 (10.23-15.43) respectively. The lowest OR for MetS was 1.93(1.61-2.30) for BSI. CONCLUSION: This study indicated that both VAI and LAP are the most valuable indices among the anthro-metabolic indices to identify MetS among the elderly in both genders. So, they could be used as proper assessment tools for MetS in clinical practice. However, the cost-benefit of these indices compared to the ATP III criteria need further studies.
RESUMO
Data mining is the process of analyzing a massive amount of data to identify meaningful patterns and detect relations, which can lead to future trend prediction and appropriate decision making. Data mining applications are significant in marketing, banking, medicine, etc. In this paper, we present an overview of data mining applications in medicine to provide a clear view of the challenges and previous works in this area for researchers. Data mining techniques such as Decision Tree, Random Forest, K-means Clustering, Support Vector Machine, Logistic Regression, Neural Network, Naive Bayes, and association rule mining are used for diagnosing, prognosis, classifying, constructing predictive models, and analyzing risk factors of various diseases. The main objective of the paper is to analyze and compare different data mining techniques used in the medical applications. We present a summary of the results and provide comparison analysis of the data mining methods employed by the reviewed articles. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s40200-021-00884-2.
