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Stroke Disease Prediction Projects using Python

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Python Projects in Stroke Disease Prediction using Deep Learning for Masters and PhD

    Project Background:
    Stroke disease prediction using deep learning is a severe medical condition, which is the sudden loss of brain function due to an interruption in blood flow or bleeding in the brain. Strokes can have devastating consequences, leading to disability or even death. Given their significant impact, an accurate and timely prediction of strokes is crucial for early intervention and prevention. The epidemiological data statistics reveal the staggering impact of strokes. They are one of the leading causes of death and long-term disability worldwide, placing an immense burden on patients lives and healthcare systems. The pressing need for better stroke prediction methods is evident when considering the toll strokes take on individuals and society. Currently, diagnosing strokes can be challenging, and delays in diagnosis can have severe consequences. By leveraging large datasets and complex patterns, deep learning models can enhance early detection and provide medical professionals valuable insights. This project aims to harness the power to develop an effective tool for predicting stroke risk, potentially saving lives and reducing the burden of stroke-related disability.

    Problem Statement

  • The context of stroke disease prediction using deep learning addressed the prevalence of imbalanced datasets with a disproportionally higher number of non-stroke cases compared to stroke cases can lead to biased models that excel at recognizing the majority class but struggle to identify individuals at risk of a stroke accurately.
  • The quality and consistency of medical data, such as electronic health records, can vary widely, impacting the reliability of predictions.
  • The inherent complexity of deep learning models poses difficulties in understanding the factors contributing to stroke predictions, which is crucial for clinical decision-making and trust in the model.
  • Additionally, the risk of overfitting is a significant concern and can become overly attuned to the training data, hindering their ability to generalize to new, unseen cases.

    • Aim and Objectives

  • Develop a deep learning-based system for early stroke prediction to support timely intervention and risk reduction.
  • Collect and preprocess diverse medical data.
  • Develop and optimize deep learning models.
  • Evaluate model performance rigorously.
  • Address imbalanced data issues.
  • Clinically validate and integrate the model.
  • Ensure continuous model improvement.

    • Contributions to Stroke Disease Prediction

    1. This project contributes to early stroke detection by developing accurate deep-learning models that can analyze a wide range of patient data, enabling healthcare professionals to identify individuals at high risk of stroke more effectively.
    2. By optimizing the deep learning models for real-time and edge deployment, the project contributes to efficient resource utilization. This ensures that the predictive tools can used on edge devices with limited computational power and make it accessible in a variety of healthcare settings.

    Deep Learning Algorithms for Stroke Disease Prediction

  • Convolutional Neural Networks (CNNs)
  • Recurrent Neural Networks (RNNs)
  • Long Short-Term Memory (LSTM) networks
  • Gated Recurrent Unit (GRU) networks
  • Feedforward Neural Networks (FNNs)
  • Capsule Networks
  • Autoencoders
  • Attention Mechanisms
  • Generative Adversarial Networks (GANs)
  • Transformer-based models

    • Datasets for Stroke Disease Prediction

  • Framingham Heart Study dataset
  • MIMIC-III Dataset
  • Kaggles Dataset
  • Atherosclerosis Risk in Communities Study dataset
  • The China National Stroke Registry dataset
  • The National Acute Stroke Israeli Survey dataset The Ohio State University Acute Ischemic Stroke dataset
  • National Health and Nutrition Examination Survey (NHANES)
  • Cerebrovascular Disease and its Consequences dataset
  • The International Stroke Trial dataset

    • Performance Metrics

  • Accuracy
  • Sensitivity
  • Specificity
  • Precision
  • F1-Score
  • Area Under the Receiver Operating Characteristic Curve
  • Area Under the Precision-Recall Curve
  • Mean Absolute Error
  • Mean Squared Error
  • Root Mean Squared Error
  • Cohens Kappa Score
  • Receiver Operating Characteristic Curves
  • Precision-Recall Curves

    • 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

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