Using various statistical techniques and principal component analysis, we identify the most important factors for stroke prediction. We conclude that age, heart disease, average glucose level, and hypertension are the most important factors for detecting stroke in patients. Furthermore, a perceptron ...
In this project, we will perform an analysis and prediction task on stroke data using machine learning and deep learning techniques. The entire process will be implemented with Python GUI for a user-friendly experience. We start by exploring the stroke dataset, which contains information about vari...
Python Project.ipynb Python Prediction Aug 8, 2022 README.md Initial commit Aug 8, 2022 Repository files navigation README Stroke-Prediction We get this real dataset from kaggle.com. Dataset Link: https://www.kaggle.com/datasets/fedesoriano/stroke-prediction-datasetAbout...
3.1.1 Brain stroke prediction dataset The Brain stroke prediction model is trained on a public dataset provided by the Kaggle [32]. This dataset comprises 4,981 records, with a distribution of 58% females and 42% males, covering age ranges from 8 months to 82 years. The dataset’s populat...
After applying Exploratory Data Analysis and Feature Engineering, the stroke prediction is done by using ML algorithms including Ensembling methods. 100% accuracy is reached in this notebook. The dataset is taken fromhttps://www.kaggle.com/datasets/jillanisofttech/brain-stroke-dataset. Also, the no...
It was carried out in 2024 using a dataset comprising 663 records of patients hospitalized at Hazrat Rasool Akram Hospital in Iran over the past five years, with data collected on December 28, 2022. This retrospective observational study aimed to analyze stroke prediction in patients. The dataset...
whereϕj(xi)– the SHAP value that shows the contribution of featurejto the prediction for each samplei. Hereiis the index of individual samples in the dataset, wherei=1,2,…,NandNis the number of records in the dataset,jis the index of individual features of each sample, wherej=1,...
In contrast behavioral, structural, and functional connectivity were obtained at all three time points in both groups (see Methods sections for details about the stroke dataset, lesion analysis, diffusion-weighted imaging (DWI), and resting-state Functional magnetic resonance imaging). Fig. 1: ...
Integrative polygenic risk prediction We investigated the risk prediction potential of stroke GWASs, alone and in combination with vascular-risk-trait GWASs, first in Europeans and East Asians, using ancestry-specific PGSs. PGSs were based on ancestry-specific and cross-ancestry GWAS summary statistics...
machine learning model to predict individuals chances of having a stroke link to heroku app: https://ml-stroke-predictions.herokuapp.com/ here you will find all code needed to run stroke predictions. in the ipynb notebooks are the model and the exploratory analysis on my dataset used to make...