heartattack

There are 14 repositories under heartattack topic.

  • advikmaniar/ML-Healthcare-Web-App

    This is a Machine Learning web app developed using Python and StreamLit. Uses algorithms like Logistic Regression, KNN, SVM, Random Forest, Gradient Boosting, and XGBoost to build powerful and accurate models to predict the status of the user (High Risk / Low Risk) with respect to Heart Attack and Breast Cancer.

    Language:Python381113

  • uditmahato/heart-attack-analysis

    "Heart Attack Analysis" - A data science project for predicting heart attacks using machine learning on health-related data.

    Language:Jupyter Notebook14100

  • RaziyeAkr/heart-attack-classification

    In this notebook, I have reviewed several classification algorithms on datasets.

    Language:Jupyter Notebook6103

  • MaryAdewunmi/Kaggle-BIPOC

    This repository contains all the Projects I lay my hands on as a Kaggle BIPOC Grantee via Kaggle learn and other sources made available to us. Thanks, Kaggle BIPOC Grant team!!

    Language:Jupyter Notebook4101

  • Devanshu0502/Heart-Attack-Prediction

    Heart Attack Prediction

    Language:Jupyter Notebook210

  • avijay24/ERFastTrack_HeartAttack

    Predictive Analysis on the ERFastTrack data to predict whether based on the symptoms the patient is going to get a heart attack.

    Language:Jupyter Notebook0100

  • BibhuPrasadPanda97/Heart-Attack-Analysis-Prediction

    Analyzing heart attack with respect to given feature and building a predictive model for finding out if a person will suffer from a heart attack or not.

    Language:Jupyter Notebook0100

  • markielokie/anomaly-detection

    Project on anomaly detection using statistical learning.

    0101

  • mustafarezk12/Heart-attack-risk-prediction-

    Streamlit web app to early predict heart attack

    Language:Python0100

  • Wahid229/Heart-Attack-Prediction-

    Language:Jupyter Notebook0100

  • ZeyTrack/Heart-Attack-Risk-Analysis.

    Predict whether patients are at high or low risk of heart attacks

    Language:Jupyter Notebook0100

  • Elzafr/Heart_Attack_Death_Prediction_using-Random_Forest

    Random Forest is a powerful tool in healthcare, helping predict heart attack fatalities. It analyzes diverse patient data, creating an ensemble of decision trees, each with unique insights. By combining these trees, it offers a more accurate risk assessment for heart attack death, potentially saving lives.

    Language:Jupyter Notebook10

  • lsawfa/HeartRiskDB

    This project involves structuring a heart attack risk dataset from Kaggle into a relational SQL database with multiple tables, setting primary and foreign keys for data integrity, and adjusting data types for optimized analysis and application use.

  • TravelXML/ML-HEART-ATTACK-EDA-PREDICTION-WITH-KERAS

    A machine learning project focused on predicting heart attacks using models like Logistic Regression, Random Forest, and XGBoost, achieving an 83.61% test accuracy. Includes comprehensive EDA, feature engineering, and hyperparameter tuning.

    Language:Jupyter Notebook