The MediAnalytics project focuses on analyzing the physical, psychological, and cognitive performance of older adults through the Virtual Patient Model Assessment dataset. The dataset provides comprehensive tracking of various health parameters, enabling in-depth exploration and analysis.
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Data Loading and Cleaning: Robust data loading and cleaning procedures ensure data integrity and consistency. Missing values are handled appropriately, and data quality is improved through preprocessing steps.
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Data Exploration: Thorough exploration of the Virtual Patient Model Assessment dataset, examining the structure, contents, and distributions of the variables.
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Visualization: The dataset is visualized through plotting graphs and histograms, providing insightful visual representations of the underlying data patterns and distributions.
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Correlation Matrix Analysis: A correlation matrix of medical parameters and adverse events uncovers relationships and dependencies between different health factors. This analysis helps identify potential risk factors and correlations contributing to adverse health outcomes.
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Anomaly Detection: Anomaly detection techniques are employed to identify outliers and unusual patterns in the dataset, flagging anomalous data points to highlight potential irregularities in the health profiles of older adults.
The MediAnalytics project provides valuable insights into the health and wellness of older adults, leveraging data-driven analysis techniques. By exploring correlations, detecting anomalies, and visualizing health trends, the project contributes to a deeper understanding of factors influencing physical, psychological, and cognitive performance in the elderly population.
- Clone the repository:
https://github.com/DSCmatter/MediAnalytics.git
- Install the required dependencies:
pip install -r requirements.txt
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Run the cmd and type: jupyter notebook, this will open your notebook on a browser, and also do not close the cmd after doing this, keep that on while working on your notebook.
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Follow the instructions in the notebook to explore and analyze the dataset or you may debug something you may find, or try something new? In anycase while doing anything, must sure Inform of all the changes you've made into this by making a pull request
- File: Virtual Patient Models_Dataset.csv
- Source
- Feel free to contribute to this project! And if you do so, don't forget to add your name on the contributors list!
- Contributors: DSCmatter
This project is licensed under the MIT License.