Machine Learning helps in predicting the Heart diseases, and the predictions made are quite accurate.

The project involves training a machine learning model (K Neighbors Classifier) to predict whether someone is suffering from a heart disease with 87% accuracy.

Heart Disease prediction using 5 algorithms

The major reason for the death in worldwide is the heart disease in high and low developed countries. The data scientist uses distinctive machine learning techniques for modeling health diseases by using authentic dataset efficiently and accurately. The medical analysts are needy for the models or systems to predict the disease in patients before the strike. High cholesterol, unhealthy diet, harmful use of alcohol, high sugar levels, high blood pressure, and smoking are the main symptoms of chances of the heart attack in humans. Data Science is an advanced and enhanced method for the analysis and encapsulation of useful information. The attributes and variable in the dataset discover an unknown and future state of the model using prediction in machine learning. Chest pain, blood pressure, cholesterol, blood sugar, family history of heart disease, obesity, and physical inactivity are the chances that influence the possibility of heart diseases. This project emphasizes to evaluate different algorithms for the diagnosis of heart disease with better accuracies by using the patient’s data set because predictions and descriptions are fundamental objectives of machine learning. Each procedure has unique perspective for the modeling objectives. Algorithms have been implemented for the prediction of heart disease with our Heart patient data set

Various classification algorithms are implemented to predict whether a person is prone to or is suffering from heart disease

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Final Year Project Heart Disease Prediction Project with all Documents.

Basic and simple ML project

Streamlit app to predict heart disease

Heart Strokes Predictions ML Model In Production

Heart disease is one of the leading causes of death worldwide, and early detection can greatly improve outcomes. This project applies machine learning to predict the likelihood of heart disease based on clinical and lifestyle data, providing a practical, beginner-friendly example of data science applied to healthcare.

The Heart Disease Prediction Using Federated Learning Algorithms project predicts heart disease using federated learning to maintain data privacy. It trains a global model using the FedAvg algorithm, sharing only model updates across clients. Built with TensorFlow and Keras, it ensures secure and decentralized training for healthcare applications

A Python project to predict heart disease

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This project will focus on predicting heart disease using neural networks. Based on attributes such as blood pressure, cholestoral levels, heart rate, and other characteristic attributes, patients will be classified according to varying degrees of coronary artery disease

Predict heart failure risk with an XGBoost ML model on clinical data. Features are engineered, scaled, and deployed via a Flask web app for real-time predictions. This project aims to support early intervention and better patient outcomes.

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A machine learning web application use to predict chances of heart disease, built with FLASK and deployed on Heroku.

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Heart Disease Prediction System using Machine Learning

Predicting Heart Disease

This repository implements the Heart Disease Prediction using various machine learning approaches

Heart disease prediction system Project using Machine Learning with Code and Report

This repo contains the code for a machine learning based prediction system where the prediction of heart disease can be done using ML techniques and several classifiers have been compared.

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Heart Disease Analysis repository