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.

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

A tool for predicting Heart Disease probability based on ML model

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

Predicting Mortality among a Cohort of patients with Heart Failure

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.

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

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.

Heart Disease Classification with Python

Machine learning project seeks to identify patterns to determine if a certain patient has heart disease

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

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

Deploying a ML model using docker in Kubernetes