# An Optimization Precise Model of Stroke Data to Improve Stroke Prediction
## 4 Conclusions  
Our study has significant implications for the prevention and management of stroke. Identifying individuals at high risk of stroke can enable targeted interventions such as lifestyle modifications, medication, and close monitoring to prevent or manage stroke. AI-based prediction models have the potential to enhance stroke prevention and management by providing accurate and efficient risk prediction.  
Our major innovation is centered around developing a new methodology for handling imbalanced datasets in stroke prediction, which has the potential to significantly decrease algorithmic bias. Traditional machine learning models can often exhibit bias in their predictions when dealing with imbalanced datasets, leading to inaccurate or incomplete results. Our new methodology leverages cutting-edge techniques from the field of machine learning to help address this issue, providing a more equitable and accurate approach to stroke prediction. By focusing on reducing algorithmic bias, our methodology can help improve the overall quality of stroke prediction models, ultimately leading to better outcomes for patients and healthcare providers alike. We introduce the data-oriented methodology to reduce algorithmic bias. Each part of the methodology is important and has its meaning--the special use of the resampling procedure, the splitting procedure with specific parameter values, and finally, the parameter \"class-weight = \"balanced\" for each model is used. Moreover, we provide the AUC values of each model and the corresponding ROC curve.  
However, our study has some limitations that need to be considered. First, our analysis was based on a single dataset, and further validation using larger and more diverse datasets is necessary. Second, our study only considered a limited set of variables, and other factors such as family history, stress, and physical activity were not included. Therefore, future studies should consider incorporating additional variables into the models.  
In conclusion, our study provides evidence that AI-based prediction models can be an effective tool for predicting stroke risk. These models can help identify individuals at high risk of stroke and enable targeted interventions to prevent or manage stroke. Further research is needed to refine and validate these models for widespread use in clinical settings.  
Author Contributions: Conceptualization, I.G.I.; methodology, I.G.I.; software, I.G.I. and Y.K.; validation, I.G.I., Y.K. and V.J.H.; formal analysis, I.G.I. and V.J.H.; investigation, I.G.I. and Y.K.; supervision, I.G.I. and V.J.H.; writing--original draft and improved manuscript, V.J.H. and I.G.I. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** The data presented in this study are openly available at [[https://www.kaggle.com/fedesoriano/stroke-prediction-dataset/](https://www.kaggle.com/fedesoriano/stroke-prediction-dataset/)]([https://www.kaggle.com/fedesoriano/stroke-prediction-dataset/](https://www.kaggle.com/fedesoriano/stroke-prediction-dataset/)) (accessed on 30 March 2023).  
**Acknowledgments:** Thanks to the reviewers for their useful comments, remarks, and constructive recommendations which have increased the value of the manuscript.