A stroke prediction model designed to assess an individual's risk of having a stroke. Stroke is a severe medical condition, and early detection can be critical for preventing or minimizing its impact. This repository provides an insightful analysis of the model's performance, evaluation metrics, and considerations for real-world applications.
The primary goal of this project is to predict whether a person could be at risk of having a stroke based on various features and medical data. Stroke prediction models like this can assist medical professionals in identifying high-risk individuals and providing timely intervention and care.
When evaluating the performance of our stroke prediction model, we consider the following key metrics:
- Accuracy: The proportion of correct predictions out of all predictions.
- Precision: The ability of the model to correctly identify positive cases (stroke risk) out of all positive predictions.
- Recall: The ability of the model to correctly identify all actual positive cases.
- F1-Score: The harmonic mean of precision and recall, providing a balance between precision and recall.
- Class-Specific Metrics: Precision, recall, and F1-score for both positive (stroke) and negative (no stroke) classes.
- Accuracy: 0.9421
- F1-Score (Class 0): 0.9702
- F1-Score (Class 1): 0.0
- Weighted Avg F1-Score: 0.9140
Model 1 demonstrates a high overall accuracy but has challenges with recall for Class 1 (stroke cases).
- Accuracy: 0.9481
- F1-Score (Class 0): 0.9475
- F1-Score (Class 1): 0.9487
- Weighted Avg F1-Score: 0.9481
Model 2 exhibits a well-balanced performance with improved recall for Class 1 and higher overall F1-scores compared to Model 1.
This project is licensed under the MIT License, which means you are free to use, modify, and distribute the code for both personal and commercial purposes. See the LICENSE file for more details.