Medicine Recommendation System Project

The Medicine Recommendation System is an intelligent healthcare application designed to predict diseases based on user-input symptoms and provide recommendations for medications, diets, precautions, and workouts. This project leverages machine learning models and a user-friendly Flask web interface to deliver personalized and reliable healthcare insights.

---

---

Table of Contents

• Usage
• Features
• Technologies Used
• Dataset
• Future Enhancements

---

Usage

1. Launch the Flask web application.
2. Navigate to the symptoms page.
3. Enter your symptoms in the input form.
4. Click the "Predict Disease" button to view:
   • Predicted disease(s)
   • Recommended medications
   • Suggested diets
   • Precautionary measures
   • Suitable workouts

---

---

Features

• Disease Prediction: Utilizes machine learning models to predict diseases based on symptoms.
• Detailed Recommendations:
  • Medications
  • Dietary suggestions
  • Precautionary tips
  • Workout plans
• Interactive Interface: User-friendly web interface built using Flask.
• Comprehensive Dataset: Integrates diverse healthcare information, including symptom severity, disease descriptions, and remedies.

---

Technologies Used

• Python: Core programming language for development.
• Flask: Framework for building the web application.
• Machine Learning Models:
  • Support Vector Classifier (SVC)
  • Random Forest Classifier
  • Decision Tree Classifier
  • Gradient Boosting Classifier
  • Multinomial Naive Bayes
  • K-Neighbors Classifier
• Pandas & Numpy: For data manipulation and preprocessing.
• Scikit-learn: For implementing and training machine learning models.

---

Dataset

The dataset includes:

• Symptoms Severity: Used for disease prediction.
• Disease Descriptions: Detailed information about diseases.
• Dietary Suggestions: Recommended diets for specific conditions.
• Medications and Precautions: Relevant medical and precautionary advice.
• Workouts: Exercises tailored to health conditions.

---

Future Enhancements

• Real-Time Updates: Incorporate dynamic updates for medical recommendations based on new research.
• User Profiles: Enable personalized healthcare insights based on user history.
• Language Support: Add support for multiple languages for broader accessibility.
• Mobile App Integration: Develop a mobile-friendly version for wider reach.

---

Links

• GitHub Code: https://github.com/Ktrimalrao/Medicine-Recommendation-System
• Dataset link: https://drive.google.com/drive/folders/1KvjW3k79J0q77o_lQsxd6WluTu84mLmx?usp=sharing
• LinkedIn link: https://www.linkedin.com/posts/k-trimal-rao-397924253_healthcare-machinelearning-flask-activity-7217209249683165184-DxMP?utm_source=share&utm_medium=member_desktop
• Azure Deployment: https://symptomsprediction.azurewebsites.net/

---

For more details and the source code, visit the project repository.
Happy Healthcare! 🩺

---

---

<----------------------------------------------------------------------------------------------------------------------------------->

$ Project By : K.Trimal Rao

<----------------------------------------------------------------------------------------------------------------------------------->

---