https://github.com/shivamsharma32/diabetes_prediction
The challenge was to effectively predict diabetes and manage patient care by analyzing extensive patient data. This included addressing issues such as extracting meaningful insights, ensuring data integrity, and efficiently managing patient records to enhance healthcare services.
https://github.com/shivamsharma32/diabetes_prediction
mssql sql
Last synced: 3 months ago
JSON representation
The challenge was to effectively predict diabetes and manage patient care by analyzing extensive patient data. This included addressing issues such as extracting meaningful insights, ensuring data integrity, and efficiently managing patient records to enhance healthcare services.
- Host: GitHub
- URL: https://github.com/shivamsharma32/diabetes_prediction
- Owner: shivamsharma32
- Created: 2024-06-19T03:14:58.000Z (12 months ago)
- Default Branch: main
- Last Pushed: 2024-06-19T03:25:08.000Z (12 months ago)
- Last Synced: 2025-01-18T12:16:10.508Z (4 months ago)
- Topics: mssql, sql
- Homepage:
- Size: 7.23 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
**Diabetes Prediction [ Intenship Project ]**
**Problem Statement**
The challenge was to effectively predict diabetes and manage patient care by analyzing extensive patient data.
This included addressing issues such as extracting meaningful insights, ensuring data integrity, and efficiently managing patient records to enhance healthcare services.
**Approach**
We utilized advanced SQL techniques to retrieve, analyze, and manipulate patient data.
This involved performing various operations such as calculating averages, ranking patients, grouping data, and updating records.
Complex queries were employed to identify patterns and correlations between health factors, while schema improvements were made to reduce redundancy and optimize performance.
**Conclusion**
Our SQL-based analysis provided valuable insights, leading to improved diabetes prediction and personalized patient management.
The continuous optimization and schema enhancements significantly boosted data integrity and query performance, enabling more accurate patient profiling and better healthcare outcomes.
