How yeakubshakil/preeclampsia works
1. This repository is a notebook-driven analysis project for preeclampsia, not a packaged application. The main deliverables are Jupyter notebooks plus a data archive named Data for ML_PE.zip, with the README only indicating the project domain.
2. The architecture is centered on a single research workflow: load clinical/ML data, perform exploratory or cohort-specific analysis, then run predictive modeling. There is no evidence of a runtime service, API layer, or deployment boundary.
3. The notebooks split into two broad lanes:
- *_analysis.ipynb notebooks for exploratory/statistical analysis
- *_predictive_modeling.ipynb notebooks for model training and evaluation
This suggests analysis outputs feed into feature selection, cohort understanding, and model development.
4. There are also “common” and “large_common” notebook variants, which likely represent two dataset scales or cohort definitions. Architecturally, that implies the same analytical pipeline is reused across different data partitions rather than being implemented once in code modules.
5. The pe_sle_hpt_hpth_* naming indicates the project compares or stratifies multiple pregnancy-related conditions or cohorts. The notebooks likely serve as the main boundary between cohort assembly and downstream analysis, with each notebook focusing on a specific population slice.
6. The Data for ML_PE.zip archive is the primary data boundary. Since no scripts or data-processing packages are listed, the notebooks probably read directly from extracted files in that archive and produce derived tables, plots, and model artifacts inside the notebook environment.
7. The predictive modeling notebooks are the main “machine learning” subsystem. Their responsibilities are likely preprocessing, feature engineering, train/test splitting, model fitting, and performance assessment, all performed interactively rather than through a reusable library.
8. The analysis notebooks are the upstream interpretation subsystem. They likely generate descriptive statistics, cohort comparisons, and data-quality checks that inform the modeling notebooks and help validate the underlying dataset.
9. The repo’s workflow is likely manual and sequential: open a notebook, load the zipped dataset, run analysis cells, then run modeling cells. That means reproducibility depends more on notebook execution order and the bundled data than on a formal codebase structure.
10. For an architecture diagram, the main nodes are: dataset archive, exploratory analysis notebooks, common/large cohort analysis notebooks, predictive modeling notebooks, and notebook outputs. The only explicit external dependency visible from the repo structure is the Jupyter runtime itself, with no sign of services, databases, or production infrastructure.
Main entry points: Data for ML_PE.zip Data archive · pe_sle_hpt_hpth_predictive_modeling.ipynb Predictive modeling · pe_sle_hpt_hpth_analysis.ipynb Analysis · pe_sle_hpt_hpth_common_analysis.ipynb Common analysis
Components 8
Data boundary
Primary input archive for the notebook workflow.
- Data archive · Bundled clinical/ML dataset read by the notebooks.
Data for ML_PE.zip
Notebook workflow
Exploratory analysis and predictive modeling notebooks reused across cohort scales.
- Analysis · Main cohort and descriptive analysis notebook.
pe_sle_hpt_hpth_analysis.ipynb - Common analysis · Shared analysis flow for common cohort data.
pe_sle_hpt_hpth_common_analysis.ipynb - Large common analysis · Analysis variant for the larger common cohort slice.
pe_sle_hpt_hpth_large_common_analysis.ipynb - Predictive modeling · Main notebook for preprocessing, model fitting, and evaluation.
pe_sle_hpt_hpth_predictive_modeling.ipynb - Large modeling · Modeling notebook tuned for the larger cohort partition.
pe_sle_hpt_hpth_large_predictive_modeling.ipynb
Other components
- Notebook outputs · Plots, tables, fitted models, and other notebook-generated artifacts.
- Jupyter runtime · Interactive notebook environment used to run the analysis workflow.
Connections 18
- Data archive → Analysis: load · Analysis notebook reads the bundled data archive.
- Data archive → Common analysis: load · Common analysis notebook reads the bundled data archive.
- Data archive → Large common analysis: load · Large common analysis notebook reads the bundled data archive.
- Data archive → Predictive modeling: load · Modeling notebook reads the bundled data archive.
- Data archive → Large modeling: load · Large modeling notebook reads the bundled data archive.
- Analysis → Predictive modeling: inform · Exploratory findings guide feature selection and modeling choices.
- Common analysis → Predictive modeling: inform · Shared cohort analysis informs the main modeling workflow.
- Large common analysis → Large modeling: inform · Large-cohort analysis informs the large-scale modeling workflow.
- Analysis → Notebook outputs: produce · Analysis notebook generates tables and plots.
- Common analysis → Notebook outputs: produce · Common analysis notebook generates tables and plots.
- Large common analysis → Notebook outputs: produce · Large common analysis notebook generates tables and plots.
- Predictive modeling → Notebook outputs: produce · Modeling notebook generates fitted models and performance metrics.
- Large modeling → Notebook outputs: produce · Large modeling notebook generates fitted models and performance metrics.
- Jupyter runtime → Analysis: run · Notebook execution environment runs the analysis notebook.
- Jupyter runtime → Common analysis: run · Notebook execution environment runs the common analysis notebook.
- Jupyter runtime → Large common analysis: run · Notebook execution environment runs the large common analysis notebook.
- Jupyter runtime → Predictive modeling: run · Notebook execution environment runs the main predictive modeling notebook.
- Jupyter runtime → Large modeling: run · Notebook execution environment runs the large predictive modeling notebook.