Keywords
Higher Education
Composite Indexes
SVM Machine Learning
Morocco
How to Cite
Abstract
This article explores the development and application of composite indexes as tools for simplifying and synthesizing complex institutional data within Higher Education Institutions (HEIs). By condensing multiple performance, growth, and success indicators into structured indexes, the approach enhances the interpretability of multidimensional datasets. These indexes are then integrated with Support Vector Machine (SVM) classifiers to identify institutional patterns and classify institutions into distinct strategic categories. The study highlights how this combination not only strengthens the predictive capacity of machine learning models but also provides clearer insights for top management. Ultimately, the integration of composite indexes with SVM models demonstrates a significant potential to support evidence-based strategic decision-making, offering HEIs a robust analytical framework for aligning institutional performance with long-term development objectives.
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