First place solution for "medicine" topic in AI Challenge 2023
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Updated
Sep 25, 2024 - Python
First place solution for "medicine" topic in AI Challenge 2023
Implementation of the study: "The Use of Deep Learning to Predict Stroke Patient Mortality" by Cheon et al.
Neural network to predict strokes.
A PyTorch model with 99.96% accuracy used for determining stroke risk, based on 16 established risk factors documented in leading medical textbooks, research papers, and guidelines from health organizations.
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.
Mini project predicting brain stroke using ML | Accuracy: 87%
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.
Этот проект представляет собой реализацию модели машинного обучения для предсказания вероятности инсульта на основе различных факторов риска.
Using Deep Learning, Object Detection to diognose and treat storke
Stroke Disease prediction
This is a Stroke Prediction Model. The app allows users to input relevant health and demographic details to predict the likelihood of having a stroke. This proof-of-concept application is designed for educational purposes and should not be used for medical advice.
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