Officially unofficial re-implementation of paper: Paint Transformer: Feed Forward Neural Painting with Stroke Prediction, ICCV 2021.

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Heart Strokes Predictions ML Model In Production

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The project uses machine learning to predict stroke risk using Artificial Neural Networks, Decision Trees, and Naive Bayes algorithms. The best-performing model is deployed in a web-based application, with future developments including real-time data integration.

Stroke Prediction Dataset | Healthcare data for predicting stroke occurrence based on patient attributes such as age, BMI, hypertension, and lifestyle factors.

Stroke is a disease that affects the arteries leading to and within the brain. A stroke occurs when a blood vessel that carries oxygen and nutrients to the brain is either blocked by a clot or ruptures. According to the WHO, stroke is the 2nd leading cause of death worldwide. Globally, 3% of the population are affected by subarachnoid hemorrhage, 10% with intracerebral hemorrhage, and the majority of 87% with ischemic stroke. 80% of the time these strokes can be prevented, so putting in place proper education on the signs of stroke is very important. The existing research is limited in predicting risk factors pertained to various types of strokes. Early detection of stroke is a crucial step for efficient treatment and ML can be of great value in this process. To be able to do that, Machine Learning (ML) is an ultimate technology which can help health professionals make clinical decisions and predictions. During the past few decades, several studies were conducted on the improvement of stroke diagnosis using ML in terms of accuracy and speed. The existing research is limited in predicting whether a stroke will occur or not. Machine Learning techniques including Random Forest, KNN , XGBoost , Catboost and Naive Bayes have been used for prediction.Our work also determines the importance of the characteristics available and determined by the dataset.Our contribution can help predict early signs and prevention of this deadly disease

SUNet: a deep learning architecture for acute stroke lesion segmentation and outcome prediction in multimodal MRI

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First place solution for "medicine" topic in AI Challenge 2023

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Machine Learning Web App Built Using Flask Deployed on Heroku

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AxionCura is a machine learning–based early stroke prediction system that analyzes patient health attributes to estimate stroke risk. It includes a trained pipeline model and a simple Python interface for running predictions locally.

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Real-time heat stroke prediction via wearable sensors (Bioengineering Senior Capstone 2016-17)

Stroke growth prediction

This repository contains a Deep Learning model using Convolutional Neural Networks (CNN) for predicting strokes from CT scans. The model aims to assist in early detection and intervention of strokes, potentially saving lives and improving patient outcomes.

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Predict whether you'll get stroke or not !!

A project on the study of receptive fields for the prediction of lesions in ischemic stroke.

Stroke prediction machine learning project

Detection (Prediction) of the possibility of a stroke in a person

Stroke Prediction with ML

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Predicting pen strokes from images.

A collection of machine learning practices covering decision trees, KNN, stroke prediction, neural networks, DBSCAN clustering, and more

[ISLES 2024 Challenge] Official Implementation for "Spatio-Temporal Deep Learning for Final Infarct Prediction using Acute Stroke CT Perfusion Data"

A web application to predict the chances of getting a stroke by a patient based on other health factors like hypertension, Smoking habit, etc.