Parkinson's Disease Prediction Model

This repository contains scripts and data to train a machine learning model to predict Parkinson's disease based on voice recordings.

What is Parkinson's Disease?

Parkinson's disease is a neurodegenerative disorder that affects movement. It develops gradually, often starting with a barely noticeable tremor in one hand. As the disease progresses, movement becomes more difficult, and tremors may affect other parts of the body. Other symptoms include stiffness or slowing of movement, impaired balance, and speech changes. Parkinson's disease primarily affects dopamine-producing neurons in the brain.

Predicting Parkinson's Disease Using Audio Samples

Parkinson's disease can affect speech patterns due to changes in vocal cords and muscles involved in speech production. Research has shown that these changes can be detected and analyzed through audio samples using machine learning techniques.

Overview

This repository contains scripts and data to train a machine learning model to predict Parkinson's disease based on voice recordings.

Dataset

The dataset used in this project consists of voice recordings from individuals, created by Max Little of the University of Oxford, in collaboration with the National Centre for Voice and Speech, Denver, Colorado, who recorded the speech signals. The original study published the feature extraction methods for general voice disorders. Each instance in the dataset includes various features extracted from these recordings, along with a binary label indicating the presence or absence of Parkinson's disease.

Exploratory Data Analysis

Understanding the Dataset

• The dataset contains features related to voice recordings, where each row represents an individual.
• Features include various acoustic measures extracted from the voice recordings.

Data Exploration

• Initial exploration involves understanding the structure of the dataset, examining column names, and gaining insights into the distribution of features.

Data Preprocessing

Data Cleaning

• Removal of unnecessary columns (e.g., ID, name) that do not contribute to the predictive model.
• Handling missing data if applicable.

Feature Engineering

• Extracting relevant features from the dataset that can contribute to predicting Parkinson's disease.

Data Standardization

• Standardizing the data using StandardScaler to bring all features to a common scale, which is crucial for models like Support Vector Machines (SVM).

Train-Test Split

• Splitting the dataset into training and testing sets (typically 80% training, 20% testing) using train_test_split from sklearn.model_selection.

Model Training (Support Vector Machine)

Support Vector Machine (SVM)

• Explanation of how SVM works in classifying data based on features.
• Implementation of SVM using SVC (Support Vector Classifier) from sklearn.svm.

Model Training

• Fitting the SVM model with the training data to learn patterns in the dataset.

Model Evaluation

• Evaluating the SVM model's performance using metrics such as accuracy, precision, and recall on the test set.

Conclusion

This README provides an overview of the steps involved in training a machine learning model to predict Parkinson's disease using voice recordings. For detailed implementation, refer to the script (Project_Parkinson's_Disease_Detection.ipynb) provided in this repository.

Dependencies

Ensure you have Python (version 3.x) installed along with the following libraries:

• numpy
• pandas
• scikit-learn (for StandardScaler, train_test_split, and SVC)

Usage

1. Clone this repository to your local machine.
2. Install the required dependencies using pip install -r requirements.txt.
3. Run Project_Parkinson's_Disease_Detection.ipynb to predict the Disease presence.

Contributing

Contributions are welcome! If you find any issues or have suggestions for improvements, please open an issue or create a pull request.

License

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