Project Background:Parkinson disease prediction using deep learning utilizes advanced artificial intelligence techniques to identify early indicators of Parkinsons disease in individuals. Deep learning models are trained on diverse data sources, including medical images, voice recordings, and clinical records. These models learn to recognize patterns and bio-markers associated with the disease. Once trained, they can analyze new data and make predictions about whether an individual is at risk of developing Parkinson disease. The primary objective is early diagnosis, enabling timely medical intervention and personalized treatment plans that can slow disease progression to improve the patients quality of life. This project holds the potential to revolutionize the way we approach Parkinsons disease diagnosis and management for ultimately offering support to those affected by this condition.
Problem Statement
- The Parkinsons disease prediction centers on the need for early and accurate diagnosis of this debilitating neurological condition that affects an individuals motor and non-motor functions, often leading to a decreased quality of life.
- Early diagnosis is critical for timely intervention and the development of personalized treatment plans. However, diagnosing Parkinsons can be challenging, as its symptoms can overlap with other conditions, and it may go undetected until it reaches an advanced stage.
- A lack of reliable and efficient diagnostic tools exacerbates the problem. Deep learning offers a promising solution with its ability to analyze diverse data sources.
- This project aims to develop predictive models that can identify subtle indicators of Parkinsons disease in medical images, voice recordings, and clinical records, thus enabling early detection and improving patient outcomes.
- It addresses a critical healthcare issue by harnessing the power of AI to provide individuals at risk with a more hopeful and proactive approach to managing Parkinsons disease.
Aim and Objectives
- Identify relevant bio-markers and data sources for Parkinsons disease prediction.
- Train deep learning models on diverse datasets, including medical imaging, voice recordings, and clinical records.
- Achieve high sensitivity and specificity in early disease detection.
- Investigate the potential for real-time monitoring and continuous assessment of Parkinsons disease progression.
- Evaluate model generalizability across diverse patient populations and demographics.
- Develop interpretable and transparent AI models to gain the trust of healthcare professionals and patients.
Contributions to Parkinsons Disease Prediction
1. In this project, one of the primary contributions is the ability to achieve an early and accurate diagnosis of Parkinsons disease that can analyze and detect subtle bio-markers and indicators of disease at an earlier stage than traditional diagnostic methods.
2. A deep learning model provides an objective and consistent method for assessing Parkinsons disease and may rely on subjective evaluations, leading to variability in diagnoses among healthcare professionals.
3. A notable contribution is integrating multimodal data for more comprehensive disease prediction by leveraging data from various sources to capture a broader spectrum of information, enhancing the accuracy of predictions.
Deep Learning Algorithms for Parkinsons Disease Prediction
- Convolutional Neural Networks (CNNs)
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Recurrent Neural Networks (RNNs)
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Long Short-Term Memory Networks (LSTMs)
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Graph Neural Networks (GNNs)
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Generative Adversarial Networks (GANs)
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Autoencoders
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Variational Autoencoders (VAEs)
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Siamese Networks
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Self-Attention Models
Datasets for Parkinsons Disease Prediction
- DATSCAN Imaging Data
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MDS-UPDRS Data
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Genomic Datasets
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Voice Recordings Datasets
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Parkinson Progression Markers Initiative (PPMI)
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UCI Parkinson Telemonitoring Data Set
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Gait and Mobility Datasets
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Clinical Electronic Health Records
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Brain Imaging Data
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Wearable Sensor Data
Evaluation Metrics
- Accuracy
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Sensitivity
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Precision
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F1-Score
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Specificity
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Mean Squared Error (MSE)
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Root Mean Squared Error (RMSE)
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Area Under the Receiver Operating Characteristic (ROC-AUC)
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Area Under the Precision-Recall Curve (PR-AUC)
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Kullback-Leibler Divergence (KL Divergence)
Software Tools and Technologies:
Operating System: Ubuntu 18.04 LTS 64bit / Windows 10
Development Tools: Anaconda3, Spyder 5.0, Jupyter Notebook
Language Version: Python 3.9
Python Libraries:
1. Python ML Libraries:
- Scikit-Learn
- Numpy
- Pandas
- Matplotlib
- Seaborn
- Docker
- MLflow
2. Deep Learning Frameworks:
- Keras
- TensorFlow
- PyTorch
