Diabetic Prediction System
LANCER
Lancer:
<= 2 resources OR <=3 weeks effort OR <=$3000 USD
Growth:2 - 4 resources AND upto 4 months OR
upto $30000 USD
Enterprise:Everything else
(Development)
Project Description
A health-focused tech startup committed to early disease detection aimed to make diabetes screening faster and more accessible through AI. The client sought an interactive tool that could analyze health inputs and provide accurate predictions without relying on internet connectivity or complex systems. The project focused on delivering a self-contained, easy-to-use prediction system driven by machine learning.
Scope Of Work
The client required a smart diabetes prediction tool that simplifies diagnosis using health indicators. The goal was to build a desktop-ready system with automated calculations and interactive output. Core areas of work included model integration, GUI development, BMI computation, environment setup, and seamless local deployment across platforms.
Our Solution
To streamline diabetes risk detection, the system was developed using Python 3.10+, enabling flexible scripting and ML integration. Here are all the key features implemented:
- Dynamic BMI Handling: Auto-computes BMI from height and weight if not entered directly.
- ML-Based Prediction Engine: Processes inputs through a trained model to assess diabetes risk.
- Cross-Platform GUI: Offers a sleek interface with minimal dependencies for instant usability.
- Simplified Launch Process: Runs directly through a single Python file, avoiding tech complexity.
- Offline Capabilities: Functions entirely without the internet, ensuring privacy and reliability.
- Streamlined Setup: Packaged with requirements.txt and virtualenv for fast deployment.
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Technologies Involved
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