Differences between the MF and MF-free groups were compared by unpaired t-test, chi-square test, Tukey’s HSD test, and Fisher’s exact test where appropriate. Potential risk variables were analyzed by univariate and multivariate logistic regression. A correlation of the distribution of the observed MF rate for each PRISM score was created with a linear regression algorithm that best fit the data points with a 95% CI. A p value less than 0.05 with a CI of 95% was considered statistically significant24 (link),25 (link). All analyses were performed using the Statistical Package for the Social Sciences (SPSS statistics version 26.0, SPSS modeler version 18, IBM Corp., Armonk, NY).
Spss modeler version 18
Manufactured by IBM
SPSS Modeler version 18.0 is a data mining and predictive analytics software tool. It provides a visual interface for building and deploying predictive models and analyzing data.
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Lab products found in correlation
7 protocols using spss modeler version 18
1
Comparing Multifocal Lens Outcomes
2
CART and CHAID for Predictive Modeling
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Data were processed by CART and CHAID algorithms in IBM SPSS Modeler version 18. To prepare and test the model, data were categorized into two groups of training and testing, about 70% of data were randomly assigned to model training and the rest 30% was considered for model assessments.22 (link)
3
Comparing Machine Learning Models
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We applied IBM SPSS statistical software version 23 for statistical analysis and IBM SPSS modeler version 18 to develop and evaluate machine learning models. We evaluated and compared the models using confusion matrix, accuracy, precision, sensitivity, specificity, F-score, and Area under the Curve (AUC). To select the best performing models, we compared the models obtained from each dataset-feature with each other based on AUC and F-score.
4
Assessing Health Service Utilization Patterns
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IBM SPSS Modeler version 18.0 was used to derive the HSU variables from the EHR data and IBM SPSS Statistics 24 was used in the statistical analyses. Health service use was measured as a yearly mean number of visits by considering the term of eligibility of the study subjects, that is, if the person died during the follow-up period, their eligibility was shorter. The yearly mean number of visits during the six-year follow-up was divided by the term of eligibility. Date of death was automatically linked to EHR data from the Statistics Finland registries. Differences in HSU between the groups were described using chi-squared (χ2) and Fischer’s exact tests. Group differences in the frequency of HSU were examined using binary logistic regression analyses, taking the T2DM group as the reference group and adjusting the model for age and gender. For the risk of death, Cox regression analysis was used.
5
Investigating Health Service Use in AUD
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IBM SPSS Modeler version 18.0 was used to derive the health-service use variables
from the EHR data, and IBM SPSS Statistics 24 was used in the statistical
analyses. Descriptive statistics were used to compare the background variables,
specialised AUD service use and MH service use of the outcome groups.
Specialised AUD service use and MH service use were measured as a yearly mean
number of visits, by considering the eligibility time of the study subjects;
eligibility time was calculated within 6 months’ accuracy for each person, and
the yearly mean number of visits was divided by the eligibility time, in order
to compare the outcome groups. For those with present AUD the follow-up time was
6 years, whereas for those who died or achieved remission, the follow-up time
varied from 6 months to 6 years. The χ2, Fischer’s exact and
Kruskal–Wallis tests were used for the group comparisons.
from the EHR data, and IBM SPSS Statistics 24 was used in the statistical
analyses. Descriptive statistics were used to compare the background variables,
specialised AUD service use and MH service use of the outcome groups.
Specialised AUD service use and MH service use were measured as a yearly mean
number of visits, by considering the eligibility time of the study subjects;
eligibility time was calculated within 6 months’ accuracy for each person, and
the yearly mean number of visits was divided by the eligibility time, in order
to compare the outcome groups. For those with present AUD the follow-up time was
6 years, whereas for those who died or achieved remission, the follow-up time
varied from 6 months to 6 years. The χ2, Fischer’s exact and
Kruskal–Wallis tests were used for the group comparisons.
6
Effects of Regular Exercise Habits
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Statistical analyses were performed using IBM SPSS Statistics, version 26.0 and IBM SPSS Modeler, version 18.0. Statistical significance was set at a two-tailed p-value of < 0.05. To compare the MCD characteristics of the participants, the chi-square test for categorical variables and t-test for continuous variables were used. The study subjects were divided into two groups, AG (active group: group of middle-aged and older individuals who have regular exercise habits) and IG (inactive group: group of middle-aged and older individuals who do not have regular exercise habits), based on whether they had regular exercise habits, and data mining was conducted separately for both groups.
7
Modeling Techniques in Social Science Research
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The model development and comparisons were performed using the Statistical Package for Social Science (IBM® SPSS® Modeler version 18.0). The datasets collected were compiled and preprocessed, including variable selection, quality exploration, cleaning, and feature engineering. Next, the random partitioning was generated with 60% for model training, followed by 40% testing of the model. The model performance was evaluated and compared, before selecting the best model fit. Figure 1 shows the actual stream in the SPSS modeler user interface used in this study.Figure 1 ![]()
SPSS Modeler stream. Dataset file containing all the variables. In data processing, a type node was used to select the variables and to assign the appropriate categories. Data audit node was used to visualise the selected variables distribution and the validity of each variable. A SetToFlag node was selected for feature engineering, which involves converting nominal variables into categorical variables: “yes or no”. The transformed data were re-analysed using the data audit node.
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