https://github.com/singhvishal003/brain_stroke_prediction
Brain Stroke Prediction according to age and Health Condition.
https://github.com/singhvishal003/brain_stroke_prediction
matplotlib numpy pandas sklearn
Last synced: 3 months ago
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Brain Stroke Prediction according to age and Health Condition.
- Host: GitHub
- URL: https://github.com/singhvishal003/brain_stroke_prediction
- Owner: Singhvishal003
- Created: 2024-11-22T13:41:45.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-11-23T07:16:11.000Z (over 1 year ago)
- Last Synced: 2025-03-23T01:25:25.658Z (over 1 year ago)
- Topics: matplotlib, numpy, pandas, sklearn
- Language: Jupyter Notebook
- Homepage:
- Size: 876 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
## Brain Stroke Prediction Model Summary
### Introduction
Stroke is a severe medical condition caused by the interruption of blood flow to the brain, leading to potential brain damage or death. Early prediction of stroke risk can significantly improve patient outcomes through timely intervention.
### Key Factors
The model considers several critical factors:
- *Age*: The risk of stroke increases with age.
- *Health Conditions*: Conditions such as hypertension, diabetes, and cardiovascular diseases are significant risk factors.
- *Symptoms*: Symptoms like sudden numbness, confusion, trouble speaking, and severe headache are crucial indicators.
### Machine Learning Techniques
The model utilizes various machine learning algorithms to predict the risk of stroke:
- *Decision Trees (DT)*
- *Random Forest (RF)*
- *Extra Trees (ET)*
- *Voting Classifiers (VC)*
### Data Handling
- *Feature Selection*: Important features include age, gender, smoking status, and health conditions.
- *Class Balancing*: Techniques like Synthetic Minority Oversampling Technique (SMOTE) are used to handle imbalanced datasets.
### Performance Metrics
The model's performance is evaluated using several metrics:
- *Accuracy*
- *Precision*
- *Recall*
- *F1 Score*
- *ROC-AUC Score*
### Conclusion
The Voting Ensemble approach, which combines multiple models, provides the best performance with an ROC-AUC score greater than 0.95. Hypertension and cardiovascular diseases are identified as major risk factors for stroke.