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.

This project studies the use of machine learning techniques to predict the long-term outcomes of stroke victims.

This is a deep learning model that detects brain stroke based on brain scans. Demonstration application is under development.

Brain stroke prediction using machine learning

This is a Flask-based web application that predicts the risk of brain stroke based on medical and personal information. It uses a Logistic Regression model trained on the Kaggle Stroke Prediction Dataset.

Prediction of brain stroke based on imbalanced dataset in two machine learning algorithms, XGBoost and Neural Network

Implementation of DeiT (Data-Efficient Image Transformer) for accurate and efficient brain stroke prediction using deep learning techniques.

Mini project predicting brain stroke using ML | Accuracy: 87%

A machine learning framework to predict brain stroke risk using demographic, medical, and lifestyle data from patient records. The pipeline features robust data preprocessing to handle class imbalance and evaluates five supervised classification models to identify key health predictors.