Leveraging Shapley Additive Explanations for Feature Selection in Ensemble Models for Diabetes Prediction Article Swipe
Prasant Mohanty
,
Sharmila Anand John Francis
,
Rabindra K. Barik
,
Diptendu Sinha Roy
,
Manob Jyoti Saikia
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/bioengineering11121215
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/bioengineering11121215
Diabetes, a significant global health crisis, is primarily driven in India by unhealthy diets and sedentary lifestyles, with rapid urbanization amplifying these effects through convenience-oriented living and limited physical activity opportunities, underscoring the need for advanced preventative strategies and technology for effective management. This study integrates Shapley Additive explanations (SHAPs) into ensemble machine learning models to improve the accuracy and efficiency of diabetes predictions. By identifying the most influential features using SHAP, this study examined their role in maintaining high predictive performance while minimizing computational demands. The impact of feature selection on model accuracy was assessed across ten models using three feature sets: all features, the top three influential features, and all except these top three. Models focusing on the top three features achieved superior performance, with the ensemble model attaining a better performance in most of the metrics, outperforming comparable approaches. Notably, excluding these features led to a significant decline in performance, reinforcing their critical influence. These findings validate the effectiveness of targeted feature selection for efficient and robust clinical applications.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/bioengineering11121215
- OA Status
- gold
- Cited By
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- References
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- Related Works
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- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W4404926645Canonical identifier for this work in OpenAlex
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https://doi.org/10.3390/bioengineering11121215Digital Object Identifier
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Leveraging Shapley Additive Explanations for Feature Selection in Ensemble Models for Diabetes PredictionWork title
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articleOpenAlex work type
- Language
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enPrimary language
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2024Year of publication
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2024-11-30Full publication date if available
- Authors
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Prasant Mohanty, Sharmila Anand John Francis, Rabindra K. Barik, Diptendu Sinha Roy, Manob Jyoti SaikiaList of authors in order
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https://doi.org/10.3390/bioengineering11121215Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.3390/bioengineering11121215Direct OA link when available
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Feature selection, Feature (linguistics), Computer science, Machine learning, Selection (genetic algorithm), Predictive modelling, Artificial intelligence, Ensemble forecasting, Ensemble learning, Model selection, Linguistics, PhilosophyTop concepts (fields/topics) attached by OpenAlex
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10Total citation count in OpenAlex
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2025: 10Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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