Thyroide prediction

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Inspiration - Thyroid disorders often go undetected due to vague symptoms and lack of early screening. Inspired by the growing potential of AI in healthcare, we aimed to create a tool that can predict thyroid conditions using patient data—helping users take early action before symptoms worsen.

What it does - The system predicts whether a person is likely to have a thyroid disorder (e.g., hypothyroidism or hyperthyroidism) based on input parameters like symptoms, blood test results, age, gender, and more. It provides a risk percentage and suggests next medical steps

How we built it - using python and typescript

Challenges we ran into - Handling missing or inconsistent data in medical records.

Ensuring the model doesn't overfit due to class imbalance.

Accomplishments that we're proud of - Achieved over 90% accuracy on the test set.

Developed a user-friendly and responsive prediction interface. Made the system accessible for non-medical users to raise awareness. Successfully applied ML to a real-world healthcare issue.

What we learned - How to handle imbalanced medical datasets. Building responsible AI systems that assist—not replace—clinical decisions.

What's next for Thyroide prediction - make it more accurate and adding new features as soon as poosible

Built With

• css
• html
• javascript
• python
• typescript