Multiple Disease Prediction Model 🧬

Welcome to the Multiple Disease Prediction Model repository! This project aims to detect whether an individual is at risk for various diseases based on their health data. The model leverages multiple machine learning algorithms to provide accurate predictions.

Table of Contents

• Introduction
• Technologies Used
• Installation
• Usage
• Model Evaluation
• Contributing
• License
• Contact

Introduction

In the modern world, early detection of diseases can save lives. The Multiple Disease Prediction Model uses various machine learning techniques to analyze patient data and predict potential health issues. This model is designed for researchers, healthcare professionals, and anyone interested in the intersection of technology and health.

Technologies Used

This project incorporates a range of powerful libraries and frameworks, including:

• NumPy: For numerical computations.
• Pandas: For data manipulation and analysis.
• Matplotlib: For data visualization.
• Scikit-learn: For implementing various machine learning algorithms.
• TensorFlow & Keras: For building and training deep learning models.
• XGBoost: For efficient gradient boosting.
• Pickle: For model serialization.
• StandardScaler: For feature scaling.

The following machine learning algorithms are utilized:

• Logistic Regression
• K-Neighbors Classifier
• Support Vector Machine (SVM)
• Random Forest Classifier
• Extra Trees Classifier
• Gradient Boosting Classifier
• AdaBoost Classifier
• Gaussian Naive Bayes
• Multi-layer Perceptron Classifier (MLP)
• XGBoost Classifier

Installation

To get started with the Multiple Disease Prediction Model, follow these steps:

1. Clone the Repository:

   git clone https://github.com/Pedro-Verissimo/Multiple_Disease_Prediction_Model.git

2. Navigate to the Directory:

   cd Multiple_Disease_Prediction_Model

3. Install Required Packages: Use pip to install the necessary libraries:

   pip install numpy pandas matplotlib scikit-learn tensorflow keras xgboost

4. Download the Model: You can download the latest model from the Releases section. After downloading, execute the model as per the instructions provided in the usage section.

Usage

To use the Multiple Disease Prediction Model, follow these steps:

1. Prepare Your Data: Ensure your data is in the correct format. The model expects a CSV file with features that correspond to the trained model.

2. Load the Model: Load the trained model using the following code:

   import pickle
   
   with open('model.pkl', 'rb') as file:
       model = pickle.load(file)

3. Make Predictions: Use the model to make predictions:

   import pandas as pd
   
   # Load your data
   data = pd.read_csv('your_data.csv')
   
   # Make predictions
   predictions = model.predict(data)
   print(predictions)

4. Visualize Results: You can visualize the predictions using Matplotlib:

   import matplotlib.pyplot as plt
   
   plt.bar(range(len(predictions)), predictions)
   plt.title('Disease Predictions')
   plt.xlabel('Samples')
   plt.ylabel('Prediction')
   plt.show()

Model Evaluation

Evaluating the performance of the model is crucial. You can use various metrics to assess accuracy, precision, recall, and F1 score. Here’s how to evaluate the model:

1. Split Your Data: Use Scikit-learn to split your data into training and testing sets:

   from sklearn.model_selection import train_test_split
   
   X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.2, random_state=42)

2. Train the Model: Train your chosen classifier:

   from sklearn.ensemble import RandomForestClassifier
   
   classifier = RandomForestClassifier()
   classifier.fit(X_train, y_train)

3. Make Predictions: Predict on the test set:

   y_pred = classifier.predict(X_test)

4. Evaluate the Model: Use metrics to evaluate:

   from sklearn.metrics import accuracy_score, classification_report
   
   print("Accuracy:", accuracy_score(y_test, y_pred))
   print(classification_report(y_test, y_pred))

Contributing

We welcome contributions to improve the Multiple Disease Prediction Model. If you want to contribute, please follow these steps:

1. Fork the repository.
2. Create a new branch:

   git checkout -b feature/YourFeature

3. Make your changes and commit them:

   git commit -m "Add some feature"

4. Push to the branch:

   git push origin feature/YourFeature

5. Open a Pull Request.

License

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

Contact

For questions or suggestions, please contact:

• Pedro Verissimo
  GitHub: Pedro-Verissimo
  Email: pedro@example.com

Thank you for checking out the Multiple Disease Prediction Model! We hope this tool aids in the early detection of diseases and contributes to better health outcomes. For more information, visit the Releases section for the latest updates.