Version 1
: Received: 17 June 2023 / Approved: 20 June 2023 / Online: 20 June 2023 (14:35:15 CEST)
How to cite:
Paliwal, S.; Parveen, S.; Alam, M.; Ahmed, J. Improving Brain Stroke Prediction through Oversampling Techniques: A Comparative Evaluation of Machine Learning Algorithms. Preprints2023, 2023061444. https://doi.org/10.20944/preprints202306.1444.v1
Paliwal, S.; Parveen, S.; Alam, M.; Ahmed, J. Improving Brain Stroke Prediction through Oversampling Techniques: A Comparative Evaluation of Machine Learning Algorithms. Preprints 2023, 2023061444. https://doi.org/10.20944/preprints202306.1444.v1
Paliwal, S.; Parveen, S.; Alam, M.; Ahmed, J. Improving Brain Stroke Prediction through Oversampling Techniques: A Comparative Evaluation of Machine Learning Algorithms. Preprints2023, 2023061444. https://doi.org/10.20944/preprints202306.1444.v1
APA Style
Paliwal, S., Parveen, S., Alam, M., & Ahmed, J. (2023). Improving Brain Stroke Prediction through Oversampling Techniques: A Comparative Evaluation of Machine Learning Algorithms. Preprints. https://doi.org/10.20944/preprints202306.1444.v1
Chicago/Turabian Style
Paliwal, S., M.Afshar Alam and Jawed Ahmed. 2023 "Improving Brain Stroke Prediction through Oversampling Techniques: A Comparative Evaluation of Machine Learning Algorithms" Preprints. https://doi.org/10.20944/preprints202306.1444.v1
Abstract
Research in brain stroke prediction is very crucial as it can lead to the development of early detection techniques and interventions that can enhance the prognosis for stroke victims. Early detection and intervention can help to minimise the damage caused by a stroke, reduce the risk of long-term complications, and enhance the general quality of life for people who have survived a stroke. Additionally, research in stroke prediction can help to identify risk factors and improve understanding of the underlying causes of stroke, which can lead to the development of better prevention strategies. Research on brain stroke prediction is ongoing and has led to the development of various models and tools for predicting the risk of stroke and detecting it early. However, the implementation and use of these tools in clinical practise vary depending on several factors, such as the availability of resources, the specific healthcare system, and the level of awareness and acceptance of these tools among healthcare providers and patients. In general, risk prediction models may be used to quickly identify individuals at high risk of stroke and target them for preventive interventions, such as lifestyle changes, medication management, and screenings. Early detection tools can be used to quickly identify stroke symptoms and initiate appropriate treatment, which can improve outcomes for stroke patients. However, it is important to note that research and development of these models and tools are ongoing, and their use in clinical practise is constantly evaluated and updated. It may take time for these tools to be widely adopted in clinical practise and to see their real-world impact. This research paper focuses on predicting brain stroke occurrence using a range of machine learning algorithms such as Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Gaussian Naive Bayes (GNB), Bernoulli Naive Bayes (BNB), and a Voting Classifier. The main emphasis of this research was to compare the effectiveness of oversampling techniques, as the data was significantly imbalanced. We have used evaluation metrics in this research to assess the accuracy, precision, recall, and other key performance indicators of a model's predictions. Area under the Receiver Operating Characteristics Curve (AUC), minority class accuracy, and majority class accuracy were used to assess the approaches.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
