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 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.

A Machine Learning model to predict Heart Disease Prediction.

A service to connect patients and doctors.

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

Classification models for heart disease prediction

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

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

A tool for predicting Heart Disease probability based on ML model

Predicting Mortality among a Cohort of patients with Heart Failure

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.

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.

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

This project analyzed patient clinical data to identify risk factors for heart disease using Extreme Learning Machine (ELM). With a dataset that includes 12 health features, the project aims to find patterns that can aid early detection.

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.

Applied Machine Learning Final Project

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

Code of the Cardiovascular Risk Prediction Project, which is used to identify risk factors for cardiovascular disease related to coronary heart disease and stroke in adults.

Repository for KNN and KMeans algorithms.

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

A jupyter notebook walking through implementing rudimentary logistic regression. Dataset downloaded from Kaggle

This project focuses on enhancing healthcare data security and privacy. We leveraged the Gaussian Differential Privacy (GDP) algorithm to protect individual patient information while enabling robust data analysis.

Heart Disease Classification with Python

No description provided.

Deploying a ML model using docker in Kubernetes

Kaggle Dataset Analysis on Exploring Important Factors to Heart Disease

A repository of the heart disease paper published on Springer