A web-based medical diagnosis and prediction application that integrates machine learning models with human input. It assists doctors in diagnosing diseases without inhibiting their expertise and final judgment.
In healthcare, accurate disease diagnosis is essential for effective treatment, achieving this technologically can be challenging due to inconsistent model predictions and the potential for human errors. In urban areas, the application can be used by specialists to confirm diagnoses or to monitor patients who are already being treated for a disease. In rural areas, where there may be a shortage of doctors, the application can be used by general practitioners to screen patients for diseases so they get timely medical treatment.
To deploy this project run
* Clone the repository on your local system.
* Open the folder in VS Code. Then open a new terminal
* Note: Use Python and pip in Windows and python3/pip3 in Mac
In Terminal:
virtalenv venv
windows: venv/Scripts.activate mac: venv/bin/activate
pip freeze > requirements.txt
python main.py
Now open localhost:3000 on your browser The problem is to provide accessible and accurate healthcare services, especially in rural areas with a shortage of doctors and medical facilities. Many people in these areas may not have easy access to healthcare, and even when they do, there can be errors or delays in diagnosis and treatment due to human error or limited resources.
Many diseases such as heart, liver and diabetes can be diagnosed and predicted based on certain features that are available from medical tests. However, doctors may not have enough time or expertise to analyze all the features and make accurate predictions. Moreover, in rural areas, there may be a shortage of doctors or specialists who can diagnose these diseases. Therefore, there is a need for a tool that can help doctors and test centers to quickly and accurately predict diseases and their severity based on the test results.
We propose the development of an AI-powered Healthcare Diagnostic and Management System. A System that enables doctors to treat patients on a priority basis reducing the chances of human error. The patient-to-doctor ratio in India is highly variable and the need for skills in the medical field can be left unattended. This is where DignoSify comes into play with its vired uses listed below:
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Doctors: Doctors can use the application to efficiently manage patient data, get disease predictions, and prioritize patients based on criticality. It reduces the risk of human error and aids in faster and more accurate diagnosis and treatment.
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Medical Test Centers: Test centers can collaborate with the application to input test results and patient data. This data contributes to the AI model's improvement.
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Rural Healthcare Centers: Healthcare centers in rural areas can use this application to bridge the gap in healthcare services. Even with limited access to medical experts, they can use the AI model to make preliminary assessments and provide better care.
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Accessible: It addresses the critical issue of healthcare accessibility, especially in underserved areas, by providing a reliable and accessible diagnostic tool.
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Accuracate: The AI models improve diagnostic accuracy, reducing the risk of misdiagnosis and human error.
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Data-driven: It encourages data collection and sharing, leading to continuous improvement in healthcare services and AI models.
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Scalable: The project has the potential to scale and adapt to changing healthcare needs and technology advancements.
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Impactful: It can save lives, improve patient outcomes, and enhance the quality of healthcare services, particularly in areas with limited resources.
Client: HTML, CSS, Javascript, BootStrap
Server: FLASK
Database Flask_SQLAlchemy
Languages Javascript, Python