September 11, 2025Open Access

Developing Optimized Machine Learning Models For Timely Prediction and Prevention of College Dropout

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The optimized random forest model achieved an accuracy of 0.85 in predicting college dropout risk.
Key predictors identified include curricular units completed in the first and second semester, influencing dropout likelihood.
Comprehensive model evaluation involved extensive data analysis, feature optimization, and performance comparison of various models.
The findings highlight the potential of machine learning to enhance the understanding of dropout predictors and help allocate resources effectively.

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Analysis shows machine learning predicts dropout risk in students, suggesting targeted interventions could improve graduation rates.

Arko Chakrabartiroy (2025) studied this question.

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