https://github.com/vartikaraj2512/diabetesdoc
**\Diabetes_Doc** 📱💡: A 2nd-year project using C++, Kotlin, and XML in Android Studio. The app estimates diabetes risk based on user symptoms via a scoring system, offering a simple, non-ML approach to health assessment. It showcases skills in UI design, rule-based prediction, and cross-language integration, adding value to early healthcare tech.
https://github.com/vartikaraj2512/diabetesdoc
c kotlin xml
Last synced: about 2 months ago
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**\Diabetes_Doc** 📱💡: A 2nd-year project using C++, Kotlin, and XML in Android Studio. The app estimates diabetes risk based on user symptoms via a scoring system, offering a simple, non-ML approach to health assessment. It showcases skills in UI design, rule-based prediction, and cross-language integration, adding value to early healthcare tech.
- Host: GitHub
- URL: https://github.com/vartikaraj2512/diabetesdoc
- Owner: VartikaRaj2512
- Created: 2024-07-29T00:43:54.000Z (almost 2 years ago)
- Default Branch: master
- Last Pushed: 2024-09-12T02:59:36.000Z (almost 2 years ago)
- Last Synced: 2025-03-04T21:25:03.548Z (over 1 year ago)
- Topics: c, kotlin, xml
- Language: Java
- Homepage:
- Size: 12.5 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
DiabetesDoc 📱💡
DiabetesDoc is a basic mobile application developed during my 2nd year, utilizing C++, Android Studio with Kotlin, and XML. This app estimates whether a patient may have diabetes based on commonly known symptoms. It calculates a score from the user's input to provide predictions, without relying on advanced technologies like Machine Learning.
Key Features:
User-Friendly Interface 🎨: Built with Kotlin and XML, the app provides an intuitive UI for easy navigation and input.
Symptom-Based Prediction 🩺: Uses a scoring system based on user-provided symptoms to estimate the likelihood of diabetes, reflecting a rule-based approach to health assessment.
C++ Integration 🔗: Implements core logic using C++, showcasing versatility and integration skills across different programming languages and platforms.
DiabetesDoc addresses a critical health issue by providing a quick and accessible tool for preliminary diabetes assessment. While basic in design, it demonstrates how traditional programming techniques can be effectively applied in the healthcare domain, highlighting the importance of innovative thinking and practical solutions in the early detection and patient awareness.
