testgithubrittttttt/HEALTHCARE-PRESCRIPTION-DETECTION-SYSTEM

Welcome to the Healthcare Prescription Detection System, a machine learning-based solution designed to assist in recommending medicines based on healthcare data. This project leverages various machine learning algorithms to predict the most appropriate prescription for patients.

HEALTHCARE-PRESCRIPTION-DETECTION-SYSTEM

Welcome to the Healthcare Prescription Detection System, a machine learning-based solution designed to assist in recommending medicines based on healthcare data. This project leverages various machine-learning algorithms to predict the most appropriate prescription for patients.

Table of Contents:

• Introduction
• Features
• Installation
• Usage
• Libraries Used
• Models Implemented
• Evaluation
• License

Introduction

The Healthcare Prescription Detection System is built to assist healthcare professionals in making accurate medicine recommendations. By analyzing patient data, the system predicts the most suitable prescriptions using various machine learning models. This can enhance the decision-making process and improve patient outcomes.

Features

• Data preprocessing and encoding
• Multiple machine learning algorithms for prediction
• Evaluation metrics for model performance
• User-friendly interface for healthcare professionals

Datasets

The project uses the following datasets:

1. Training.csv: Contains the training data for the machine learning models.
2. symptoms_df.csv: Contains data on symptoms.
3. precautions_df.csv: Contains data on precautions for various conditions.
4. workout_df.csv: Contains data on recommended workouts.
5. description.csv: Contains descriptions of various conditions.
6. medications.csv: Contains data on medications and their uses.
7. diets.csv: Contains dietary recommendations for various conditions.

Ensure these datasets are available in the project's directory before running the scripts.

Installation

To get started with the Healthcare Prescription Detection System, follow these steps:

• Clone the repository: git clone https://github.com/yourusername/healthcare-prescription-detection-system.git

• Install and Importing the required libraries:

  1. import pandas as pd
  2. from sklearn.model_selection import train_test_split
  3. from sklearn.preprocessing import LabelEncoder
  4. from sklearn.datasets import make_classification
  5. from sklearn.model_selection import train_test_split
  6. from sklearn.svm import SVC
  7. from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
  8. from sklearn.neighbors import KNeighborsClassifier
  9. from sklearn.naive_bayes import MultinomialNB
  10. from sklearn.metrics import accuracy_score, confusion_matrix
  11. import numpy as np

Usage

• Prepare your dataset and ensure it is in the correct format.
• Review the output for accuracy scores and confusion matrix results.
• The following Python libraries are utilized in this project: pandas numpy scikit-learn Models Implemented

The project includes several machine learning models for predicting prescriptions:

• Support Vector Classifier (SVC)
• Random Forest Classifier
• Gradient Boosting Classifier
• K-Nearest Neighbors (KNN) Classifier
• Multinomial Naive Bayes

Evaluation

The models are evaluated based on their accuracy and confusion matrices. The accuracy score and confusion matrix provide insights into the performance of each model. from sklearn.metrics import accuracy_score, confusion_matrix

Example of evaluation

accuracy = accuracy_score(y_test, y_pred) conf_matrix = confusion_matrix(y_test, y_pred)

License

This project is licensed under the MIT License. See the LICENSE file for more details.

Follow and Star

If you found this project helpful, please follow me and star the repository on GitHub!

Follow me on GitHub = testgithubrittttttt | Star this repository

Thank you for using the Healthcare Prescription Detection System! We hope this project aids in your healthcare endeavors. If you have any questions or feedback, feel free to reach out.

Happy coding!