Welcome to our Health Analytics Data Exploration Repository! This repository is dedicated to fostering research and innovation in the field of health informatics, particularly focusing on diabetes risk prediction and telemedicine during the COVID era. We provide datasets and machine learning code to enable comprehensive analysis and model building.
- Description: This dataset comprises medical records of patients categorized as diabetic and non-diabetic. It's designed to aid in the early prediction of diabetes risk.
- Fields: Includes patient demographics, and medical history indicators.
- Format: CSV
- Description: This dataset captures the dynamics of telemedicine consultations during the COVID-19 pandemic, offering insights into healthcare delivery changes.
- Fields: Contains information on patient consultations, types of medical services rendered.
- Format: CSV
- Languages Used: Python
- Libraries/Frameworks: Scikit-learn, Pandas, NumPy, TensorFlow (if applicable)
- Features: Scripts for data preprocessing, exploratory data analysis, model training (classification algorithms), and evaluation metrics.
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Risk Prediction Modeling: Utilize the diabetes dataset to develop predictive models for early-stage diabetes risk. Experiment with different classification algorithms and compare their performance.
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Telemedicine Service Analysis: Analyze the telemedicine dataset to understand how healthcare services evolved during the pandemic. Focus on patient demographics, types of services, and satisfaction levels.
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Cross Analysis: Investigate any correlations between diabetes risk and the usage of telemedicine services during COVID-19.
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Time Series Analysis: Perform a time series analysis on the telemedicine data to identify trends and patterns over the pandemic period.
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Feature Engineering: Explore creating new features from existing data to improve model accuracy for diabetes risk prediction.
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Data Visualization: Create interactive visualizations to represent findings from both datasets.
We welcome contributions from the community. If you have ideas for additional analyses or improvements to the existing codebase, please feel free to fork the repository and submit a pull request.