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 service to connect patients and doctors.
A Machine Learning model to predict Heart Disease Prediction.
Classification Model (End to End Classification of Heart Disease - UCI Data Set)
A tool for predicting Heart Disease probability based on ML model
Classification models for heart disease prediction
Predicting Mortality among a Cohort of patients with Heart Failure
This is a blog of how machine learning algorithms are used to detect if a person is prone to heart disease or not.
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.
My effort has been to do this project with logistic regression
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.
Heart Disease Classification with Python
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.
Identification system for the molecular basis of coronary heart disease powered by AI ( Artificial Intelligence ) and machine learning algorithms.
A jupyter notebook walking through implementing rudimentary logistic regression. Dataset downloaded from Kaggle
Deploying a ML model using docker in Kubernetes
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.
Machine learning project seeks to identify patterns to determine if a certain patient has heart disease
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