# Synthesizing Explainability Across Multiple ML Models for Structured Data
## 6 Conclusions  
We tackle the under-addressed challenge of combining multiple explainable model outputs on the same dataset. Today, practitioners routinely train diverse models and extract feature-importance scores. However, aggregation is often performed ad hoc (e.g., eyeballing top-k overlaps), inviting inconsistency and bias. To address this, we introduce Weighted Importance Score and Frequency Count (WISFC), a simple, reproducible framework that numerically fuses two dimensions:  
1. Magnitude (WIS): the average, optionally performance-weighted rank of each feature across models. 2. Consistency (FC): the proportion of models ranking each feature within the top-k.  
By jointly scoring features on impact and support, WISFC surfaces predictors that are both influential and robust. In a clinical case study, it recovered known risk factors (e.g., glucose, BMI) that single models might miss and demoted spurious, model-specific signals. Unlike informal overlap checks, WISFC provides clear thresholds (e.g., \"90% of models and a top-5% average rank\") for asserting feature reliability. Implementation is straightforward and broadly applicable: any set of importance measures (coefficients, Gini, SHAP) can be normalized, weighted, and aggregated. Whether in bioinformatics (ensuring gene-importance findings hold across algorithms) or finance (identifying risk factors that persist across models), WISFC enhances trust by anchoring explanations in consensus rather than chance. As explainable AI gains traction in high-stakes domains, robust aggregation tools are essential. WISFC represents a step toward ensemble interpretability. Just as ensemblesboost predictive accuracy, they can also stabilize explanations. Future work might integrate WISFC with interaction- or counterfactual-based explainers or develop statistical confidence bounds for its scores, further strengthening its rigor. Ultimately, the core message remains: consistency breeds trust. By prioritizing features agreed upon by multiple models, WISFC transforms disparate interpretations into a cohesive, reliable narrative. We encourage its adoption in any setting where multiple models and limited data demand a reproducible, scientifically grounded approach to feature-importance aggregation. Conceptualization, E.V. ; methodology, E.V., L.Z. and O.V. ; validation, E.V., L.Z., O.V. and T.R. ; formal analysis, E.V., L.Z. and O.V. ; resources, H.G. and C.M.G. ; data curation, L.Z. and C.M.G. ; writing--original draft preparation, E.V. and O.V. ; writing--review and editing, E.V., L.Z., O.V., H.G., J.G.R. and T.R. ; supervision, J.G.R. and T.R. ; project administration, C.M.G. ; funding acquisition, C.M.G. and T.R. All authors have read and agreed to the published version of the manuscript. Data for this study were obtained from the Transitions of Care Stroke Disparities Study (TCSD-S), supported by the National Institutes of Health/National Institute of Mental Health (NIH/NIMH) under ClinicalTrials.gov Identifier NCT03452813. The study protocol was approved by the Institutional Review Board of the University of Miami Protocol ID20170892. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Data Availability Statement: Restrictions apply to the availability of these data. Data were obtained from Hospitals participating in the Florida Stroke Registry and are available on request from Transitions of Care Stroke Disparities Study (TCSD-S) and the Florida Stroke Registry (FSR) [[https://loridastrokecollaboration.org/](https://loridastrokecollaboration.org/)]([https://loridastrokecollaboration.org/](https://loridastrokecollaboration.org/)) (accessed on 15 June 2025) with the permission of respective hospitals. During the preparation of this manuscript, the authors used ChatGPT (OpenAI GPT-4) for text optimization and language refinement. The authors reviewed and edited all AI-generated content and take full responsibility for the accuracy and integrity of this publication. Author [PERSON] is employed by Beevadoo e.U. Co., Ltd.