Figure 1.
The gender distribution in the stroke dataset
Figure 2.
Bar chart illustrating the association between hypertension, age, glucose level, and BMI
Figure 3.
Correlation Matrix of the features within the stroke dataset
Table 1.
The overall performance of the six applied ML models
| Models | Accuracy | Precision | Recall | F1 | AUC |
|---|---|---|---|---|---|
| Logistic Regression | 0.775 | 0.757 | 0.809 | 0.782 | 0.846 |
| KNN | 0.920 | 0.874 | 0.981 | 0.925 | 0.973 |
| SVM | 0.894 | 0.906 | 0.878 | 0.892 | 0.968 |
| Decision Tree | 0.947 | 0.952 | 0.941 | 0.947 | 0.947 |
| Random Forest | 0.970 | 0.992 | 0.947 | 0.969 | 0.994 |
| XGBoost | 0.970 | 0.988 | 0.952 | 0.970 | 0.992 |
Figure 4.
The Area Under the Curve for all applied models for stroke prediction
Figure 5:
LIME results of two different scenarios (patient at low risk vs patient at high risk) and the factors influencing the outcome. The higher the absolute amount of weight, the more significant the impact on the projected result.