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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.
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.
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:

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