There are 2 repositories under stroke-prediction topic.
First place solution for medicine topic in AI Challenge 2023
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
Predict whether you'll get stroke or not !!
Detection (Prediction) of the possibility of a stroke in a person
Implementation of the study: "The Use of Deep Learning to Predict Stroke Patient Mortality" by Cheon et al.
Stroke-GFCN: segmentation of Ischemic brain lesions
DATA SCIENCE PROJECT ON STROKE PREDICTION- deployment link below 👇⬇️
Stroke prediciton with EDA, data preprocessing, model building and sampling
machine learning model to predict individuals chances of having a stroke. See users view of app here: https://ml-stroke-predictions.herokuapp.com/
Stroke Prediction Using Machine Learning (Classification use case)
🥈🎉 Silver Award Winner and Presented at IEEE Conference 📝 .This project implements a comprehensive pipeline for real-time ECG (Electrocardiogram) data processing and analysis, integrating IoT devices.
A small ML program which predicts stroke probability based on medical status of the patient
Stroke analysis, dataset - https://www.kaggle.com/datasets/fedesoriano/stroke-prediction-dataset. For analysis i used: mlp classifier, k-means clustering, k-neighbors classifier. Libraries: tensorflow, scikit-learn.
My first stroke prediction machine learning logistic regression model building in ipynb notebook using python.
Some machine learning algorithms to predict whether a person would have a stroke or not.
Evaluate stroke risk using logistic regression and decision tree models
Comparing 10 different ML classifiers and using the one having best accuracy to predict the stroke risk to user. Fetching user details through web app hosted using Heroku.
Neural network to predict strokes.
2022년 1학기 개인 프로젝트 : 뇌졸증 환자 예측 모델·분석
Stroke Prediction App
Mechine Learnig | Stroke Prediction
R shiny app for stroke prediction
This repo evaluates Logistic Regression, Random Forest, and Support Vector Machine models for predicting stroke risk. Implemented in Python, the project includes data pre-processing, model training, and performance metric calculations
Using Machine Learning Techniques to Predict the Occurrence of Stroke
Prediction of stroke in patients using machine learning algorithms.
Regression Analysis using dataset from different Industries
An end-to-end machine learning project for stroke prediction
Comparison of Machine Learning and Deep Learning Techniques for Stroke Prediction
Stroke Prediction System Using ML
A patient's chance of stroke was predicted using analysis of a medical dataset. Project conducted in collaboration with a partner, Phillis Duong.
This project aims to make predictions of stroke cases based on simple health data. Supervised machine learning algorithm was used after processing and analyzing the data. The model has predicted Stroke cases with 92.00% of sensitivity.
The project aims at displaying the charts/plots of the number of people affected by stroke based on the input parameters like smoking status, high blood pressure level, Cholesterol level, obesity level in some of the countries.
Stroke Disease prediction