Predicting and Analyzing Early Onset of Stroke Using Advanced Machine Learning Classification Technique
- Title
- Predicting and Analyzing Early Onset of Stroke Using Advanced Machine Learning Classification Technique
- Creator
- Kumari P.; George J.; Nair A.M.; Paul Alapatt B.; Baby R.; Jose J.
- Description
- Around the world, stroke is the leading cause of death. When blood vessels in the brain rupture, they cause damage. Alternatively, blockage in a blood vessel that supplies oxygen and other nutrients may also lead to this disease. This study uses various machine learning models to predict whether someone will have a stroke or not. Different physiological features were taken into account by this study while using Logistic Regression; Decision Tree Classification; Random Forest Classification; K-Nearest Neighbors (KNN); Support Vector Machine (SVM); Nae Bayes classifier algorithm; and XGBoost classification algorithm - these were used for six different models to ensure accurate predictions are made. We will accomplish the finest exactness with Bayes cv look which may be a hyper-tuning classifier with 92.87%. This consideration can be utilized for future work by doing the increase and include designing on the dataset. It is constrained to literary information, so it might not continuously be right for foreseeing stroke. so utilize the datasets that contain pictures and work on those datasets. 2024 IEEE.
- Source
- 10th International Conference on Electrical Energy Systems, ICEES 2024
- Date
- 2024-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Bayes cv search; Decision Tree Classification; K_Nearest neighbors; Logistic Regression; Machine learning; Nae Bayes classification; Random Forest Classification; Stroke; Support Vector Machine
- Coverage
- Kumari P., CHRIST (Deemed to be University), India; George J., CHRIST (Deemed to be University), India; Nair A.M., Luxsh Technologies Pvt Ltd, United Kingdom; Paul Alapatt B., CHRIST (Deemed to be University), India; Baby R., CHRIST (Deemed to be University), India; Jose J., CHRIST (Deemed to be University), India
- Rights
- Restricted Access
- Relation
- ISBN: 979-835035377-8
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Kumari P.; George J.; Nair A.M.; Paul Alapatt B.; Baby R.; Jose J., “Predicting and Analyzing Early Onset of Stroke Using Advanced Machine Learning Classification Technique,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 25, 2025, https://archives.christuniversity.in/items/show/19000.