This advanced deep learning project predicts the birth weight of newborns using maternal and environmental factors, addressing critical health concerns such as low birth weight due to malnutrition or lifestyle factors. Built with Python, TensorFlow, and integrated into a Flask web application, it is ideal for final-year engineering students, aligning with IEEE standards and offering significant applications in healthcare and public health awareness.
Utilizes a deep neural network to predict newborn birth weight based on maternal health, lifestyle, and environmental factors.
Processes features like maternal age, smoking habits, alcohol consumption, and nutritional status for accurate predictions.
A user-friendly web interface built with HTML, CSS, and JavaScript, powered by Flask, allows users to input data and receive birth weight predictions.
Incorporates robust preprocessing techniques to handle missing data, normalize features, and enhance model performance.
Achieves high accuracy in predictions through optimized deep learning models tailored to healthcare data.
Built using Python, TensorFlow 2.3+, scikit-learn, and Flask for a robust and scalable solution.
Utilizes curated datasets with maternal and fetal health records, including features like maternal BMI, age, and lifestyle factors.
Employs a deep neural network with multiple layers, optimized for regression tasks to predict continuous birth weight values.
Assists medical professionals in identifying at-risk pregnancies and planning interventions to improve birth outcomes.
Promotes awareness about the impact of lifestyle factors like smoking and alcohol on newborn health.
Enables early detection of low birth weight risks, supporting preventive healthcare measures.
Supports public health initiatives by providing data-driven insights for maternal and child health programs.
When you purchase this project, you gain access to a complete, end-to-end solution designed to ensure your success. Here's what we offer:
Receive fully functional and tested Python code, including the Flask web app, ready for implementation.
We assist in implementing the project on your system, ensuring smooth integration and providing full support throughout the process.
Get detailed documentation, including reports, PPTs, and raw data for research papers, ensuring a successful presentation and publication.
Benefit from ongoing mentorship and support, with assistance for any errors or improvements needed throughout your project journey.
This is one of the best IEEE project ideas for final-year students, combining deep learning, healthcare analytics, and web development. We provide complete frontend and backend codes, along with detailed explanations to help you understand the project thoroughly. Our support extends to content for your report and IEEE paper publication.