Health Check ✔ is a Machine Learning Web Application made using Flask that can predict mainly three diseases i.e. Diabetes, Heart Disease, and Cancer.

A Machine Learning model to predict Heart Disease Prediction.

A service to connect patients and doctors.

A comprehensive exploration of machine learning techniques and data science best practices applied to the UCI Heart Disease dataset. Focusing on data preprocessing, exploratory analysis, and predictive modelling to identify key factors in heart disease. Part of Big Data Management and Analytics (BDMA) program.

Identification system for the molecular basis of coronary heart disease powered by AI ( Artificial Intelligence ) and machine learning algorithms.

A tool for predicting Heart Disease probability based on ML model

Exploratory Data Analysis (EDA) of heart disease mortality in the United States (2019–2021), uncovering geographic, sex, and race/ethnicity disparities using NCHS data.

Classification models for heart disease prediction

This project applies machine learning to predict heart disease using clinical data. It covers data preprocessing, model building, and performance evaluation, aiming to support early diagnosis and healthcare decision-making through data-driven insights and AI-based prediction techniques.

Classification Model (End to End Classification of Heart Disease - UCI Data Set)

Predicting Mortality among a Cohort of patients with Heart Failure

CardioSafe AI: A Streamlit web app leveraging machine learning to predict heart disease risk. Features interactive patient data inputs, real-time risk analysis with visual feedback, and emergency health guidelines. Includes developer profile links and dynamic UI elements. Ideal for healthcare AI demonstrations and preventive cardiology insights. ❤️

This is a blog of how machine learning algorithms are used to detect if a person is prone to heart disease or not.

My effort has been to do this project with logistic regression

This is a machine learning project that uses various machine learning alogorithms to predict whether a patient is suffering from heart disease or not. Here I am using variour machine learning algorithms like Random Forest classifier, XGBClassifier, GaussianNB, Decision Tree Classifier, K-Nearest Neighbours and Logistic Regression.

A machine learning application, deployed using Flask, is designed to identify the presence of heart disease in patients by analyzing various medical features.

CARDIOsetu is a web application designed to monitor individual heart health. It uses API integration to enable voice-to-text input for accessibility, making it easier for individuals with verbal and visual disabilities to interact with the app.

BEGINNER - This is a classification project for the subject "Data Mining" in the 3rd year of Statistics (SSE) at the University of Milano-Bicocca.

Heart Failure/Heart Disease Prediction through Statistical Analysis and Machine Learning

Applied Machine Learning Final Project