The primary goal of this project is to develop a neural network capable of accurately detecting diseases based on symptoms provided by users. Leveraging advanced technologies and methodologies, the aim is to create a robust system that aids in early disease detection and improves healthcare accessibility.
Develop a neural network that can analyze symptoms provided by users and accurately predict potential diseases at an early stage.
Create a user-friendly interface that allows individuals to easily input symptoms and receive timely and accurate disease predictions, thereby improving access to healthcare services.
Achieved 95% Precision in disease prediction by leveraging state-of-the-art machine learning algorithms and methodologies.
- Python: Programming language for neural network development.
- Kaggle: Data source for collecting disease-related datasets.
- Keras: High-level neural networks API for model development.
- Tensorflow: Open-source machine learning framework for building and training neural networks.
- Natural Language Processing (NLP): Techniques utilized for processing symptom-related textual data.
- Classification Methodologies: Various classification algorithms employed for disease prediction.
- Early Intervention : By enabling early disease detection, the project aims to facilitate timely intervention and treatment, potentially saving lives and reducing healthcare costs.
- Enhanced Healthcare Access :Providing a user-friendly platform for disease prediction can empower individuals, especially in underserved areas, to seek medical advice and care promptly.
- Research Advancement : The project's methodologies and findings could contribute to the broader field of healthcare and machine learning research, leading to further innovations in disease prediction and prevention.