Stroke Prediction using Machine Learning
The Brain stroke is a cardiovascular disease that occurs when the blood flow becomes abnormal in head region. main cause of this abnormality is disability or death. There are two types of dataset: Stroke and Normal. User have to gave input image and model will predict that person have stroke or not.
However, the use of ML algorithms for brain stroke classification, the model is trained on large dataset of image have stroke or normal. then we just import the model in testing file and test that particular image.
The early detection of stroke warning symptoms is crucial in reducing the severity of a stroke. The primary goal of this study was to utilize deep learning and machine learning techniques to forecast the probability of an early-stage stroke occurrence. To evaluate the effectiveness of the algorithm, a reliable dataset for stroke prediction was obtained from the Kaggle website. Various classification models, such as Extreme Gradient Boosting (XGBoost), Ada Boost, Light Gradient Boosting Machine, Random Forest, Decision Tree, Logistic Regression, K Neighbors, SVM – Linear Kernel, Naive Bayes, and deep neural networks (3-layer and 4-layer ANN), were employed in this study for classification tasks.
Brain Stroke Detection using Deep Learning
Software Requirements :-
- Coding Language : Python
- Implementation: Software Framework.
- Operating system : Windows 10 / 11.
- Graphical User Interface : Tkinter
Hardware Requirement:-
- Input Devices : Keyboard, Mouse.
- System : Pentium i3 Processor.
- Hard Disk : 500 GB.
- RAM : 4 GB.
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