Stroke Disease Detection and Prediction Using Robust Learning Approaches
| Autor: | Mohammad Monirujjaman Khan, Mohammad Sajibul Bari, Nur Alam, Sami Bourouis, Tahia Tazin, Nahian Nakiba Dola |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Medicine (General)
Article Subject Computer science Biomedical Engineering Decision tree Health Informatics Machine learning computer.software_genre Logistic regression Machine Learning R5-920 Robust learning Robustness (computer science) Classifier (linguistics) Medical technology Range (statistics) medicine Humans R855-855.5 Stroke business.industry Brain medicine.disease Random forest Logistic Models Surgery Artificial intelligence business computer Algorithms Research Article Biotechnology |
| Zdroj: | Journal of Healthcare Engineering Journal of Healthcare Engineering, Vol 2021 (2021) |
| ISSN: | 2040-2295 |
| DOI: | 10.1155/2021/7633381 |
| Popis: | Stroke is a medical disorder in which the blood arteries in the brain are ruptured, causing damage to the brain. When the supply of blood and other nutrients to the brain is interrupted, symptoms might develop. According to the World Health Organization (WHO), stroke is the greatest cause of death and disability globally. Early recognition of the various warning signs of a stroke can help reduce the severity of the stroke. Different machine learning (ML) models have been developed to predict the likelihood of a stroke occurring in the brain. This research uses a range of physiological parameters and machine learning algorithms, such as Logistic Regression (LR), Decision Tree (DT) Classification, Random Forest (RF) Classification, and Voting Classifier, to train four different models for reliable prediction. Random Forest was the best performing algorithm for this task with an accuracy of approximately 96 percent. The dataset used in the development of the method was the open-access Stroke Prediction dataset. The accuracy percentage of the models used in this investigation is significantly higher than that of previous studies, indicating that the models used in this investigation are more reliable. Numerous model comparisons have established their robustness, and the scheme can be deduced from the study analysis. |
| Databáze: | OpenAIRE |
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