NeuraMed
We developed a ML and DL based disease prediction web application known as NeuraMed with a chatbot for helping with generic disease prediction from symptoms.
Created on 25th August 2024
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NeuraMed
We developed a ML and DL based disease prediction web application known as NeuraMed with a chatbot for helping with generic disease prediction from symptoms.
The problem NeuraMed solves
NeuraMed: Revolutionizing Disease Prediction through AI
1.NeuraMed is a powerful AI-driven web application designed to make healthcare more accessible and accurate through advanced machine learning (ML) and deep learning (DL). The application offers users a chatbot for predicting diseases based on symptoms, alongside specialized models for specific disease predictions.
2.NeuraMed’s capabilities include high-accuracy predictions for pneumonia from chest X-ray images and skin cancer from skin cell images, leveraging state-of-the-art image classification models. It can also assess a person’s likelihood of being pre-diabetic and predict conditions like heart disease and Parkinson's disease. All of these models have demonstrated an accuracy of over 90% on both training and testing datasets, providing reliable diagnostic support.
3.The goal of NeuraMed is to streamline complex diagnostic processes, reduce human error, and offer accessible healthcare insights to users. With its high-accuracy models, NeuraMed stands to make disease prediction more efficient and potentially safer, empowering both patients and healthcare professionals to make informed decisions quickly.
Challenges we ran into
Developing NeuraMed was both rewarding and challenging, as we worked to create an AI-driven web application capable of accurately predicting diseases using machine learning (ML) and deep learning (DL). Throughout this process, we encountered several significant challenges.
One of the key difficulties was achieving consistent accuracy above 90% across our models for pneumonia, skin cancer, pre-diabetes, heart disease, and Parkinson’s disease. This required continuous experimentation with model architectures and hyperparameters to strike the right balance between complexity and generalization, ensuring strong predictive performance without overfitting.
Sourcing high-quality datasets was another challenge. NeuraMed’s performance depends on the accuracy and diversity of the data it uses. Curating reliable datasets from trusted medical sources was essential to ensure our models generalize well across diverse populations, but it required careful effort to maintain dataset quality and balance.
Debugging and error resolution during model training was also a significant task. Addressing issues such as data inconsistencies and convergence problems in the models required thorough analysis, but this effort helped enhance the models' stability and performance.
Feature engineering added further complexity. Identifying and weighing the most predictive features, such as symptoms or image data, demanded careful analysis to ensure that the models made accurate predictions based on the right indicators.
Lastly, integrating the frontend with the AI models posed technical challenges. We needed to ensure a seamless flow of data between the user interface, chatbot, and backend models while maintaining fast, accurate responses to ensure a user-friendly experience.
Despite these obstacles, overcoming them helped refine NeuraMed into a reliable system capable of delivering highly accurate disease predictions to both patients and healthcare professionals.