Obesity Risk Prediction using Machine Learning | Python Final Year Project | IEEE Project 2024
Obesity Risk Prediction using Machine Learning | Python Final Year Project | IEEE Project 2024 - 2025.
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🔗Email: [email protected],
🌐Website: https://www.jpinfotech.org
🚀IEEE Base Paper Title: DeepHealthNet: Adolescent Obesity Prediction System Based on a Deep Learning Framework.
📌Our Proposed Project Title: Obesity Risk Prediction using Machine Learning
💡Implementation: Python.
🔬Algorithm / Model Used: XGBoost Classifier, Stacking Classifier.
🌐Web Framework: Flask.
🖥️Frontend: HTML, CSS, JavaScript.
💰Cost (In Indian Rupees): Rs.5000/
📘OUR PROPOSED ABSTRACT:
Obesity has become one of the leading public health concerns globally, contributing to various chronic conditions like diabetes, cardiovascular diseases, and certain cancers. Early identification of obesity risk is crucial for implementing preventive measures and personalized health interventions. This project presents a machine learning-based approach to predict obesity risk using two advanced classification models: XGBoost Classifier and Stacking Classifier. The system uses Python as the coding language, with Flask as the web framework, and an intuitive front-end built using HTML, CSS, and JavaScript.
Two powerful machine learning models, XGBoost Classifier and Stacking Classifier, were employed in the system to enhance the accuracy of predictions. The XGBoost Classifier, known for its gradient boosting capabilities and efficient handling of large datasets, achieved a perfect training accuracy of 100% and a test accuracy of 98%. Similarly, the Stacking Classifier, which combines multiple base models to improve predictive performance, also achieved 100% training accuracy and 98% test accuracy. These results demonstrate the robustness and reliability of the proposed system in predicting obesity risk with high precision.
📍REFERENCE:
Ji-Hoon Jeong, Associate Member, IEEE, In-Gyu Lee , Sung-Kyung Kim , Tae-Eui Kam, Seong-Whan Lee , Fellow, IEEE, and Euijong Lee, “DeepHealthNet: Adolescent Obesity Prediction System Based on a Deep Learning Framework”, IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, VOL. 28, NO. 4, APRIL 2024.
❓Frequently Asked Questions:
1. What motivated you to work on the Obesity Risk Prediction project?
2. What datasets did you use for this project?
3. Which machine learning models did you implement in the system, and why?
4. How did you pre-process the data before training the models?
5. What evaluation metrics did you use to assess model performance?
6. Can you explain the significance of the features used in the dataset?
7. What challenges did you encounter during the project, and how did you overcome them?
8. How can the results of your prediction be utilized in real-world applications?
9. What are the future directions for your project?
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