The process of creating a system for detecting Parkinson’s disease has been explained by our well-equipped team. It is a neurodegenerative disorder which impacts the movements in the body. This disease is handled effectively by diagnosing it earlier. Deep Neural Networks (DNNs) is a trustworthy method for recognizing Parkinson’s disease in an individual. It involves different kinds of data, voice recordings, handwriting samples and gait analysis. This method was acquired as a main focus for recognizing Parkinson’s disease. Various research ideas and topics are provided by matlabsimulation.com we are more grateful to assist you and guide in all your Parkinson Disease Detection Using Deep Neural Networks research encounters.
The basic structure of DNNs for detecting the Parkinson’s disease with the help of voice recordings is enlisted below,
- Data Collection :
UCI ML Parkinson’s datasets are employed by us for this system. For biomedical voice research, it gathers the biomedical voice dimensions from the healthy individuals and compared with Parkinson’s affected people.
- Data Preprocessing :
- Feature Normalization: The features are measured for possessing the zero mean and unit variance.
- Data Splitting: We split the data into three sets,
- Training set
- Validation set and
- Test set
- Model Architecture :
Build the structure of DNN (Deep Neural Networks). A sample program using TensorFlow/Keras is depicted below,
python
from keras.models import Sequential
from keras.layers import Dense, Dropout
model = Sequential()
# Input layer
model.add(Dense(128, activation=’relu’, input_shape=(number_of_features,)))
# Hidden layers
model.add(Dense(256, activation=’relu’))
model.add(Dropout(0.5))
model.add(Dense(128, activation=’relu’))
model.add(Dropout(0.5))
# Output layer
model.add(Dense(1, activation=’sigmoid’)) # Binary classification: Parkinson’s or No Parkinson’s
- Model Compilation and Training :
Our model is compiled with a suitable optimizer, loss function and metrics. Then we start the training process of our model:
python
model.compile(optimizer=’adam’, loss=’binary_crossentropy’, metrics=[‘accuracy’])
model.fit(X_train, y_train, batch_size=32, epochs=100, validation_data=(X_val, y_val))
- Model Evaluation :
On test set, we explore the performance of the model:
python
loss, accuracy = model.evaluate(X_test, y_test)
print(f”Accuracy: {accuracy * 100:.2f}%”)
- Model Improvement:
- Hyperparameter Tuning: The parameters are modified that includes learning rate, number of layers, and the number of units in particular layer and dropout rate.
- Feature Engineering: Instructive features are derived by us from the voice recordings or other data types occupied are reviewed like handwriting specimens.
- Deployment :
Combine the model with a clinical decision support system after we are satisfied with our model performance. Remember that the model is just supported as an additional tool and it does not fill the space of medical experts.
Pointers:
- Regularization: This includes dropout layers or L1/L2 regularization for protecting the model from overfitting.
- Balanced Dataset: We assure that the count of Parkinson’s samples is approximately similar to the count of non-affected samples. Accomplish methods such as oversampling, under sampling, or synthetic data generation for balancing the dataset.
- Model Interpretability: It provides the medical consequences and this is very significant for understanding the decision made by models. SHAP or LIME are the tools that help us in learning.
- Collaboration: For verifying the result and explaining the circumstances, it is crucial to discuss with field experts like neurologists that lead our model for advancement and confirmation.
While DNNs help in detecting the diseases earlier and observations, it is crucial to recollect the judgements about the patient’s health that includes the complicated clinical explorations by the experts in the medical field.
Parkinson Disease Detection Using Deep Neural Networks Thesis Ideas
By getting experts help we shape your research work as per your needs which adds academic success. Thesis ideas will be listed from international journal tailored by your request. Nearly three to five thesis ideas will be shared in which you can select the best we also offer complete support for entire research work.
Some of the samples of our Parkinson Disease Detection Using Deep Neural Networks Thesis Ideas are listed contact us and get high rank in your academics with expert’s touch.
- Parkinson’s Disease Detection Using Hybrid LSTM-GRU Deep Learning Model
- A novel sample and feature dependent ensemble approach for Parkinson’s disease detection
- A voice feature extraction method based on fractional attribute topology for Parkinson’s disease detection
- A significantly enhanced neural network for handwriting assessment in Parkinson’s disease detection
- Parkinson’s disease detection and classification using EEG based on deep CNN-LSTM model
- Artificial Intelligence Model for Parkinson Disease Detection Using Machine Learning Algorithms
- High-resolution superlet transform based techniques for Parkinson’s disease detection using speech signal
- Addressing smartphone mismatch in Parkinson’s disease detection aid systems based on speech
- A novel feature extraction method based on TQWT and instantaneous energy variation for Parkinson’s disease detection
- PDD-ET: Parkinson’s Disease Detection Using ML Ensemble Techniques and Customized Big Dataset
- A systematic review of adaptive machine learning techniques for early detection of Parkinson’s disease
- Early detection of Parkinson’s disease using machine learning
- Parkinson Disease Detection using Feed Forward Neural Networks
- Parkinson’s Disease Detection Using Acoustic features from Speech recordings
- Automatic Fall Detection system for patients with Parkinson’s disease
- PD-Net: Multi-Stream Hybrid Healthcare System for Parkinson’s Disease Detection using Multi Learning Trick Approach
- Detecting Parkinson’s disease from Speech signals using Boosting Ensemble Techniques
- Detection of Parkinson disease for handwriting dataset using deep learning algorithms
- An Effective Feature Selection Technique for Detecting Parkinson’s Disease Using Binary Whale Optimization Algorithm
- Detecting Parkinson’s Disease using Machine Learning
- Machine Learning Based Parkinson’s Disease Detection from Enhanced Speech
- Detection of Parkinson Disease using Machine Learning
- Deep Transfer Learning Based Parkinson’s Disease Detection Using Optimized Feature Selection
- Parkinson’s disease detection from speech analysis using deep learning
- Parkinson’s Disease Detection And Classification Of Stages From Drawing Patterns Using Deep Learning
- Keystroke-Dynamics for Parkinson’s Disease Signs Detection in an At-Home Uncontrolled Population: A New Benchmark and Method
- mm-Wave wireless radar network for early detection of Parkinson’s Disease by gait analysis
- Time-Frequency Analysis of Speech Signal Using Wavelet Synchrosqueezing Transform for Automatic Detection of Parkinson’s Disease
- Lax-net: Freezing of Gait Detection in Parkinson’s Disease Using LSTM with Attention and XGBoost
- A Smartphone-based Application to Detect Parkinson’s Disease Using Audio
- Development of Healthcare Model Using AR-DVAE with Mayfly-MLP-BPN for Parkinson’s Disease Detection
- Easy Park: Mobile Application for Parkinson’s Disease Detection and Severity Level
- Parkinson’s Disease Detection using Machine Learning Algorithm
- Static-Dynamic Temporal Networks for Parkinson’s Disease Detection and Severity Prediction
- Deep Learning-based Early Parkinson’s Disease Detection from Brain MRI Image
- Detection of Parkinson’s Disease and Its Severity Using Deep Learning
- Inertial-Based Gait Metrics During Turning Improve the Detection of Early-Stage Parkinson’s Disease Patients
- Exemplar-Based Sparse Representations for Detection of Parkinson’s Disease From Speech
- An Interpretable Deep Learning Optimized Wearable Daily Detection System for Parkinson’s Disease
- Parkinson’s Disease Symptom Detection using Hybrid Feature Extraction and Classification Model
