Project Background:
The Blood Pressure Prediction stems from the pressing need to develop accurate and efficient models for predicting blood pressure levels, a vital indicator of cardiovascular health. High blood pressure is a leading risk factor for heart disease, stroke, and other cardiovascular complications, contributing significantly to global morbidity and mortality rates. Traditional methods for blood pressure measurement, such as sphygmomanometry, are invasive, intermittent, and may not capture the dynamic nature of blood pressure fluctuations throughout the day.
Consequently, there is a growing interest in leveraging machine learning and predictive modeling techniques to develop non-invasive and continuous blood pressure prediction models. This work recognizes the challenges posed by the complex physiological mechanisms underlying blood pressure regulation and the diverse factors influencing blood pressure dynamics, including lifestyle, genetics, and environmental factors. Additionally, this may include exploring the potential of wearable sensors, physiological signals, and electronic health records as data sources for blood pressure prediction models.
Problem Statement
- Traditional methods for blood pressure measurement are invasive and may not provide continuous monitoring of blood pressure dynamics.
- Existing blood pressure measurement techniques offer intermittent snapshots of blood pressure levels, which may not capture the full spectrum of blood pressure fluctuations throughout the day.
- Blood pressure is regulated by complex physiological mechanisms influenced by various factors, including lifestyle, genetics, and environmental factors, making accurate prediction challenging.
- Blood pressure levels vary significantly among individuals, and predictive models must account for this variability to provide accurate and personalized predictions.
- Access to large-scale, high-quality datasets for blood pressure prediction is limited, hindering the development and evaluation of predictive models.
- Accurate blood pressure prediction is critical for early detection and management of hypertension, a leading risk factor for cardiovascular diseases.
- However, existing predictive models may not meet the clinical standards for accuracy and reliability.
- There is a need for real-time prediction of blood pressure levels to enable timely interventions and personalized healthcare. However, current models may lack the capability for continuous monitoring and prediction.
Aim and Objectives
- To develop accurate and non-invasive predictive models for blood pressure levels.
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Develop machine learning algorithms capable of accurately predicting blood pressure levels.
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Incorporate diverse factors influencing blood pressure dynamics, including lifestyle, genetics, and environmental factors, into predictive models.
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Evaluate the performance and reliability of predictive models using real-world datasets.
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Investigate the feasibility of continuous, non-invasive blood pressure monitoring for timely interventions and personalized healthcare.
Contributions to Blood Pressure Prediction
- Development of accurate predictive models for blood pressure levels.
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Incorporation of diverse factors influencing blood pressure dynamics into predictive models.
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Evaluation of predictive model performance using real-world datasets.
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Exploration of continuous, non-invasive monitoring techniques for timely interventions and personalized healthcare.
Deep Learning Algorithms for Blood Pressure Prediction
- Long Short-Term Memory (LSTM)
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Convolutional Neural Network (CNN)
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Recurrent Neural Network (RNN)
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Transformer
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Gated Recurrent Unit (GRU)
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Attention Mechanism
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Autoencoder
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Variational Autoencoder (VAE)
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Deep Belief Network (DBN)
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Generative Adversarial Network (GAN)
Datasets for Blood Pressure Prediction
- Framingham Heart Study dataset
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MIMIC-III dataset
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NHANES dataset
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PhysioNet dataset
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Health eHeart dataset
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OpenAPS dataset
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Apnea-ECG dataset
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Longitudinal Health Records dataset
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Cardiology Challenge dataset
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UCI Machine Learning Repository - Cardiovascular dataset
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
