This repository contains a machine learning model for predicting the presence or absence of Parkinson's disease based on biomedical voice measurements. Parkinson's disease is a neurodegenerative disorder affecting movement, with symptoms including tremors, stiffness, and impaired balance. Early detection is crucial for effective management and treatment.
The dataset used in this project consists of biomedical voice measurements collected from individuals with and without Parkinson's disease. These measurements, obtained from sustained phonations, include various acoustic features.
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Data Preprocessing: The dataset is cleaned and preprocessed to handle missing values, scale features, and potentially engineer new features for improved model performance.
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Feature Selection: Relevant features are selected to build robust predictive models. Techniques such as correlation analysis and feature importance are employed to identify the most informative features.
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Model Selection: Several machine learning algorithms are evaluated and compared to identify the best-performing model. Algorithms such as Support Vector Machines (SVM), Random Forest, Logistic Regression, etc., are considered.
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Model Training: The selected model is trained on the preprocessed dataset using techniques such as cross-validation to ensure generalizability and minimize overfitting.
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Model Evaluation: The trained model is evaluated using various performance metrics such as accuracy, precision, recall, and F1-score to assess its effectiveness in predicting Parkinson's disease.
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Hyperparameter Tuning: Model hyperparameters are tuned using techniques such as grid search or random search to optimize model performance further.
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Model Validation: The final model is validated on unseen data to ensure its reliability and generalization ability.
- Clone the repository:
git clone https://github.com/your-username/parkinsons-disease-prediction.git
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- Install the required dependencies:
pip install -r requirements.txt
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- Run the main script to train and evaluate the model:
python main.py
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- Incorporate more advanced feature engineering techniques to extract additional insights from the voice measurements.
- Explore ensemble learning methods to combine the predictions of multiple models for improved accuracy.
- Develop a web-based application or API for easy deployment and accessibility of the predictive model.
- Enhance the model's interpretability by employing techniques such as SHAP (SHapley Additive exPlanations) values or LIME (Local Interpretable Model-agnostic Explanations).
- Collect and incorporate additional data sources, such as demographic information or medical history, to enhance the predictive power of the model.