The Shapiro-Wilk test was used to determine whether the data conformed to the normal distribution. Normal distribution data were expressed as (mean ± SD), and t-tests were used for comparison between groups. Non-normal distribution data were expressed as median (M) and interquartile range (P25, P75), and the difference was compared by two independent sample rank sum tests. Count data were expressed as the number of cases (percentage), and comparison between groups was analyzed using the χ2 test or Fisher exact probability. p < 0.05 was considered statistically significant. The LR model was performed by Statistical Package for Social Sciences (SPSS) 22.0 (IBM, Armonk, NY, USA). Python software was used to build RF, SVM, and CNN models. The performance measurement of the models was used to compare the generalization ability of the classifier. Precision and recall were more important than other evaluation indicators in disease risk prediction. We used the following indicators for the performance evaluation of the four models: AUC of ROC, accuracy, precision, specificity, recall, and F1-score.
Statistical package for social sciences spss 22
Manufactured by IBM
Sourced in United States
SPSS 22.0 is a software package used for statistical analysis. It provides tools for data management, analysis, and presentation. The software supports a variety of statistical procedures, including descriptive statistics, bivariate analysis, and multivariate analysis. SPSS 22.0 is commonly used in social science research, market research, and business analytics.
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Lab products found in correlation
2 protocols using statistical package for social sciences spss 22
1
Comparison of Statistical and Machine Learning Models in Disease Risk Prediction
2
Comparing Body Fat Measurements
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Descriptive statistics was used by mean and standard deviation. Data distribution presented normality according to the kurtosis and skewness values, in a range from -2 to + 2 (George and Mallery, 2002) . To compare means between sexes, the t-Student test for independent samples was used. The proportion of categorical variables according to sex was compared by the chi-square test. Pearson's correlation was used to test the relationship between body fat percentage by DXA and ADP with anthropometric indicators. The variables that presented statistically significant correlation (p <0.05) were tested in the linear regression analyses. In the multiple linear regression analysis, physical activity, bone age, use of ART and use of PI were considered as control variables. In addition, regression analyses were estimated using the regression coefficient (β), with 95% confidence interval (95% CI), standard error and adjusted determination coefficient. For all analyses, 5% significance level was adopted. Analyses were stratified by sex and the IBM® Statistical Package for Social Sciences (SPSS) 22.0 (Chicago, United States) was used.
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