Rani Saha,, Priyanka
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Student
Research Areas
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Biography and Research Information
OverviewAI-generated summary
Rani Saha Priyanka's research focuses on county-level diagnosed diabetes prediction. Their work involves developing and analyzing code and data to understand the distribution and potential predictors of diabetes at a county level. This research contributes to public health efforts by providing tools for analyzing health data and identifying areas that may require targeted interventions.
Metrics
- Publications: 4
Positions
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Student 2025–presentArkansas State University Mathematics ORCID
Selected Publications
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Predicting county-level diagnosed diabetes prevalence in the United States using explainable gradient boosting and geographic interpretation (2026)
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County-Level Diagnosed Diabetes Prediction: Analysis Code and Data (2026)
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County-Level Diagnosed Diabetes Prediction: Analysis Code and Data (2026)
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Border-Region Status and Diagnosed Diabetes Prevalence in Texas: A Cross-Sectional Ecological Analysis (2026)
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Texas Border Diabetes Analysis: Code and Data (2026)
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Texas Border Diabetes Analysis: Code and Data (2026)
Collaboration Network
Top Collaborators
- Texas Border Diabetes Analysis: Code and Data
- Texas Border Diabetes Analysis: Code and Data
- Border-Region Status and Diagnosed Diabetes Prevalence in Texas: A Cross-Sectional Ecological Analysis
- County-Level Diagnosed Diabetes Prediction: Analysis Code and Data
- County-Level Diagnosed Diabetes Prediction: Analysis Code and Data
Showing 5 of 6 shared publications
- Texas Border Diabetes Analysis: Code and Data
- Texas Border Diabetes Analysis: Code and Data
- Border-Region Status and Diagnosed Diabetes Prevalence in Texas: A Cross-Sectional Ecological Analysis
- County-Level Diagnosed Diabetes Prediction: Analysis Code and Data
- County-Level Diagnosed Diabetes Prediction: Analysis Code and Data
Showing 5 of 6 shared publications
- Border-Region Status and Diagnosed Diabetes Prevalence in Texas: A Cross-Sectional Ecological Analysis
- County-Level Diagnosed Diabetes Prediction: Analysis Code and Data
- County-Level Diagnosed Diabetes Prediction: Analysis Code and Data
- Predicting county-level diagnosed diabetes prevalence in the United States using explainable gradient boosting and geographic interpretation
- Texas Border Diabetes Analysis: Code and Data
- Texas Border Diabetes Analysis: Code and Data
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