https://github.com/hchandeepa/stroke-risk-prediction-system-a_machine_learning_approach

Machine learning models for stroke incidence prediction.
https://github.com/hchandeepa/stroke-risk-prediction-system-a_machine_learning_approach

jupyter-notebook logistic-regression machine-learning prediction python

Last synced: about 21 hours ago
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Machine learning models for stroke incidence prediction.

• Host: GitHub
• URL: https://github.com/hchandeepa/stroke-risk-prediction-system-a_machine_learning_approach
• Owner: HChandeepa
• License: mit
• Created: 2024-03-08T13:00:50.000Z (8 months ago)
• Default Branch: main
• Last Pushed: 2024-08-25T13:47:17.000Z (3 months ago)
• Last Synced: 2024-08-25T15:01:58.597Z (3 months ago)
• Topics: jupyter-notebook, logistic-regression, machine-learning, prediction, python
• Language: Jupyter Notebook
• Homepage:
• Size: 12.7 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
I successfully developed and implemented efficient binary classification machine learning models for stroke incidence prediction. Leveraging the synthetic minority over-sampling technique (SMOTE) method was crucial for ensuring balanced datasets, which is essential for the formulation of effective algorithms in stroke prediction. I conducted a comprehensive analysis, employing logistic regression and creating a user-friendly web application for stroke risk estimation. The project involved extensive research on stroke, its risk factors, diagnosis, treatment, and recovery. By utilizing cutting-edge technologies such as artificial intelligence and machine learning, I contributed to advancing early diagnosis and intervention in stroke cases, potentially saving lives and improving outcomes for stroke survivors worldwide.