Statistical Methods
SPSS 26.0 and Modeler 18.0 (IBM Corporation, Armonk, New York, United States) programs were used to analyze variables. Univariate data’s suitability to normal distribution was evaluated with the Shapiro-Wilk Francia test, while variance homogeneity was assessed with the Levene test. Independent-Samples T-test was used together with Bootstrap results. In contrast, the Mann-Whitney U test was used together with Monte Carlo to compare two independent groups according to quantitative data. One-Way Anova test, one of the parametric methods, was used to compare multiple separate groups according to quantitative data. The Tukey HSD test was used for post hoc analysis. Kruskal-Wallis H Test, one of the nonparametric tests, was used with Monte Carlo simulation technique results, and Dunn’s test was used for Post Hoc analysis. In comparison of categorical variables, Pearson Chi-Square Exact results were analyzed, while the Fisher-Freeman-Holton test was tested with the Monte Carlo Simulation technique.
Logistic Regression, Support Vector Machine, Random Forest, K-nearest Neighbor Algorithm, Simple (Native) Bayes Classification, and Neural Network (Multilayer Perceptron-Radial Basis) were used to find and predict the variable with the highest significance in the patient and control groups. Neural Network (Multilayer Perceptron) analysis, which is the most successful model among these methods, was used. Gradient descent was used for optimization algorithm, Hyperbolic tangent as hidden layer activation function, Softmax as output Layer activation function were used. The Mini-Batch method was used for the training data selection, and a 70% Trial set was set as a 30% Testing set. Quantitative variables are mean ± SD (standard deviation) in tables. Moreover, Median (Percentile 25% / Percentile 75%), while categorical variables were shown as n (%). Variables were analyzed at a 95% confidence level, and a p-value of less than 0.05 was considered significant.