Inspiration
Real-world healthcare and lifestyle datasets commonly face serious data-quality issues, such as impossible zero values in clinical metrics like insulin and BMI. This led us to create an advanced data cleaning and exploratory analytics solution using Python to improve resource management in employee wellness programs.
What it does
More than just a standard data cleaning process, DiaPredict Analytics has implemented a business-oriented layer that allows us to convert continuous clinical data into structured analyses based on international medical standards (such as WHO nutrition levels and oral glucose levels). The output of our solution consists of a framework which combines population realities and shows how structured data analytics can help mitigate health insurance risks.
How we built it
Using standard Python libraries, we managed to create an accurate analytic version of the original database Pima Indians Diabetes. The data processing architecture is built with Pandas for structural filtering, NumPy for validation, and Matplotlib and Seaborn to provide data visualization.
Challenges we ran into
As students of management, it was our greatest difficulty to bridge the gap between raw and severely corrupted clinical data arrays and strategic business implementations. The process of overcoming the problem of missing data while respecting the realistic class balance required appropriate pipeline construction.
Accomplishments that we're proud of
We have achieved the creation of a completely transparent and reproducible data cleansing script based on Python data libraries. We are particularly proud of being able to turn difficult-to-handle clinical data into a clear and structured dashboard that provides a live correlation of continuous physiological variables with standard health benchmarks.
What we learned
We found out about the importance of data quality checks and how this process becomes the main trouble in the health management sector. We also understood how data feature creation helps to convert complex continuous parameters into accessible population insights.
What's next for DiaPredict Analytics
The immediate goal will be to improve our analysis pipeline so that it can work not only with the public dataset but also with larger health registries. We plan to conduct a few stakeholder interviews to fine-tune the dashboard layout, introduce innovative imputation practices, and finally test a variety of machine learning classifiers in compliance with the rules for working with health data.
