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Your-Health-Assistent

Your-Health-Assistent

Leader posted 1 min read

With diabetes affecting nearly 1 in 3 adults in Pakistan, the growing health crisis highlights the urgent need for early awareness and preventive healthcare. According to recent reports, Pakistan has one of the highest diabetes prevalence rates globally.

As an Electrical Engineering student at National University of Sciences and Technology with a strong interest in AI and intelligent systems, I developed an AI-powered Diabetes Prediction System that predicts whether a person is likely to be diabetic or non-diabetic based on health-related inputs.

Features of the project:
• AI/ML-based diabetes prediction
• Health suggestions and preventive guidance
• User-friendly interactive web interface
• Focus on awareness and early detection

This project reflects my growing interest in AI for healthcare and how intelligent technologies can be used to solve real-world problems.

Live Project:
https://your-health-assistent-c5bgjggd7xtabmvozv3qxt.streamlit.app/

I would really appreciate it if you could kindly review my project and share suggestions for improvement. Feedback from developers, researchers, and professionals would help me learn, improve, and grow further in the field of AI and healthcare technology.

2 Comments

•

Really interesting project. What model did you use for the prediction system, and how did you handle data quality and accuracy validation?

1 vote
•

@[Spyros] > Thank you!
For the prediction system, I used a Logistic Regression machine learning model trained on healthcare-related datasets to identify diabetes risk patterns. The workflow included data preprocessing, handling missing values, feature scaling, and cleaning inconsistent inputs to improve data quality.
To validate accuracy, I used separate training and testing datasets and evaluated the model using accuracy metrics and prediction consistency checks. I also compared outputs during testing to ensure reliable predictions.

Alongside the ML model, I focused on creating a simple and user-friendly interface that also provides basic health suggestions based on the prediction results.

The project is still evolving, and I’m continuously improving the model performance, UI/UX, and overall reliability through testing and feedback.

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