Parkinsons disease detection using machine learning is an impactful application in medical diagnostics and neuro health analysis.
This project examines vocal measurements such as pitch frequency jitter shimmer and other biomedical voice features to determine whether a person is likely to have Parkinsons disease.
Using Python and machine learning the model analyzes subtle variations in speech patterns which helps in early detection and supports doctors in clinical decision making.
Project Overview
- Machine learning based classification system for Parkinsons disease prediction
- Uses Support Vector Machine SVM as the main classification model
- Built using Python and widely used scientific libraries
Libraries Used
- Pandas for data handling and preprocessing
- NumPy for numerical computation
- Matplotlib and Seaborn for exploratory visualizations
- Scikit Learn for SVM modeling feature scaling and evaluation
- StandardScaler for feature normalization
- Train Test Split for performance validation
Dataset Details
The dataset contains biomedical voice measurements collected from individuals both healthy and diagnosed with Parkinsons disease.
Important features include
- MDVP frequency measures
- MDVP jitter and shimmer values
- Harmonic to noise ratios
- Non linear dysphonia parameters
- Various vocal fold signal metrics
The target column indicates
- One meaning Parkinsons disease present
- Zero meaning Parkinsons disease not present
Preprocessing Steps
- Inspected the dataset and checked for missing values
- Separated the features and labels
- Scaled the input features using StandardScaler to improve model training
- Split the dataset into training and testing sets
Model Building
- Support Vector Machine SVM chosen as the classification model
- Model trained to identify small variations in vocal features that signal Parkinsons disease
- Evaluated using accuracy score and confusion matrix
- SVM performs well for biomedical classification tasks involving complex numerical patterns
Performance and Accuracy
- Accuracy score calculated for training and test data
- Confusion matrix used to assess true positive and false negative predictions
- Model demonstrates high performance in detecting Parkinsons disease from voice signals
Prediction Flow
1 User enters the vocal measurement values for one patient
2 Input data is standardized using the same scaler used during training
3 SVM model predicts disease status
- One means Parkinsons disease detected
- Zero means Parkinsons disease not detected
Deployment Possibilities
- Can be deployed using Flask or Streamlit for live prediction
- Useful for early screening tools and neurological health applications
- Can be integrated into telemedicine systems for remote patient evaluation
Key Takeaways
- Complete end to end biomedical classification pipeline implemented
- Demonstrates how speech signal analysis can help identify neurological disorders
- Shows practical value in supporting medical professionals with automated predictions
Future Enhancements
- Experiment with advanced algorithms such as Random Forest XGBoost or Neural Networks
- Apply hyperparameter tuning and cross validation
- Add visualizations for feature importance and voice patterns
- Expand system into a full medical diagnostic support tool