Enhancing Patient Care through AI-Driven Disease Prediction
The aim is to develop a comprehensive system that utilizes artificial intelligence (AI) and machine learning (ML) techniques to predict diseases based on user-provided symptoms and manage patient information effectively.
The following machine learning algorithms were utilized for disease prediction:
- Decision Tree: Constructs a tree-like structure to make decisions based on feature values.
- Random Forest: Ensemble learning method that constructs multiple decision trees and outputs the mode of the classes.
- Support Vector Machine (SVM): Classifies data points by finding the hyperplane that best separates different classes.
- K-Nearest Neighbors (KNN): Classifies data points based on the majority class among their k nearest neighbors.
- Naive Bayes: Probabilistic classifier based on Bayes' theorem, assuming independence between features.
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Disease Prediction: Predict diseases based on user-provided symptoms.
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Patient Report Generation: Generate detailed patient reports including predicted disease, description, precautions, and medication suggestions.
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Django Integration: Implemented using Django framework, incorporating various features such as dynamic input chatbox, voice search, and location integration.
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User-Friendly Interface: Intuitive user interface with interactive features for seamless user experience.
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AI Health Assistant (ChatBot) : The AI Health Assistant provides tailored health guidance and information, harnessing cutting-edge AI models and advanced NLP techniques. Users can explore health tips, uncover causes, symptoms, and precautions, discover home remedies, receive medication guidance, and locate nearby hospitals.
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Clone the repository:
git clone https://github.com/sanu0711/Healthcare-Intelligence.git -
Navigate to the project directory:
cd DiseasePrediction -
Install dependencies:
pip install -r requirements.txt
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent