# Synthesizing Explainability Across Multiple ML Models for Structured Data
## 4 Case Study and Cross-Domain Examples
### Case Study: WISFC on a Small Clinical Dataset  
To make the WISFC method more concrete, we present a case study drawn from our original research context (a small structured dataset in healthcare) [19]. The dataset in question involves, for example, a clinical cohort with a few hundred patients and a set of candidate predictors (demographics, lab tests, etc.) for a health outcome. Such datasets are often small due to the cost of data collection in medicine, and they pose a challenge for ML--many features might seem plausibly relevant, but with limited data, different models will latch onto different signals. In our case study, we applied a brute-force modeling strategy: we trained a diverse set of models, including a logistic regression, a decision tree, a random forest, a gradient boosting machine (XGBoost), and a support vector machine. We used repeated cross-validation to mitigate overfitting and obtained feature importance for each model using appropriate methods (coefficients for logistic, Gini importance for tree-based models, SHAP values for the SVM via a Kernel SHAP explainer, etc.). Each model thus produced a ranked list of 20 candidate predictors by importance. The results were illuminating. Before applying WISFC, if we looked at each model separately, we saw considerable variation in their top predictors. For instance, Logistic Regression highlighted Age, Blood Pressure, and BMI as its top three; Random Forest gave the highest importance to Age as well, but also to Glucose Level and Cholesterol; XGBoost prioritized Glucose Level, Insulin, and Blood Pressure; the SVM (with SHAP) pointed to BMI, Age and a specific Genetic Marker as top features. The overlap between any two models' top 3 was, at best, one or two features. A naive approach might have taken the intersection of all these sets--which would leave only Age and Blood Pressure as common to most. Indeed, those two features were the only ones that every model had ranked in its top five. Does that mean only Age and Blood Pressure are truly important? Common sense and domain knowledge suggest otherwise--Glucose and BMI, for example, are well-known risk factors in this medical context, and it was puzzling that not all models agreed on them. This is where WISFC helped uncover the nuance. When we applied the Weighted Importance Score and Frequency Count aggregation, using equal weights for simplicity (each model 20% weight), we found that Age and Blood Pressure did indeed have high _WIS_ and perfect _FC_ (5/5 models). However, we also found Glucose Level had a high _WIS_--in fact, it was second only to Age in _WIS_--even though Glucose did not appear in the top 3 of the two models. Those two models still ranked Glucose moderately high (say, 4 th and 6 th), and the other three models ranked it very high (1 st or 2 nd). So its average rank was very good, and it had \(FC=4/5\) (80%). BMI similarly had \(FC=4/5\) and a solid _WIS_ (within the top 5 overall). On the other hand, the Genetic Marker that the SVM found important had \(FC=1/5\) (20%)--no other model thought it was important--and consequently, its _WIS_ was low despite one strong vote. WISFC thus produced a ranked list of features by _WIS_, alongside their _FC_ values, which we can summarize: e.g., Age (\(WIS1.4\), \(FC100\%\)), Glucose (2.2, 80%), Blood Pressure (3.0, 100%), BMI (4.0, 80%), cholesterol (5.6, 60%), Insulin (7, 40%), etc., down to features that never appeared in any top-10 (\(FC0\%\)). Comparing this WISFC-derived list to what we know from medical literature, we found it much more aligned with established risk factors. Age and Blood Pressure were expected, but WISFC rightly kept Glucose, BMI, and Cholesterol in the important set, whereas a simplistic intersection of top features might have wrongly discarded some of those due to disagreement between models. In other words, WISFC was able to extract a richer set of predictive variables than the strict common intersection while still being selective enough to ignore one-off spurious features.