Up to 90% accuracy with just 5 features using KNN algorithm and PCA for feature engineering. The dataset contained less than 1000 observations. The model's accuracy could be improved using more observations, further hyperparameter optimization and feature engineering

Projeto de Mineração de dados de mortes por problema cardíaco

Conducted research and developed a system under Dr Jixin Ma on the comparison of numerous classification models to predict coronary heart disease using past medical data from the UCI Machine Learning Repository. Executed the project using tools such as Pandas, NumPy, Matplotlib, Seaborn, and Scikit-Learn, and evaluated the classification models …

Health is real wealth in the pandemic time we all realized the brute effects of COVID-19 on all irrespective of status. We are required to analyze this health and medical data for better future preparation.

This repository contains a project for predicting heart attacks using a dataset of patient information. The project includes data preprocessing, feature engineering, model training using Random Forest Classifier.

It is a medical chatbot that will provide quick answers to FAQs by setting up rule-based keyword chatbots.

Predict heart disease using ML linear regression. Built with Python (Streamlit), React, Node.js, JWT, and Google OAuth for authentication.

Artificial intelligence in medical science plays a vital role and That's why I build this Model to predict Hert Disese.

Data Science Foundations I | Exploratory Data Analysis in Python | Inspect, Clean, and Validate a Dataset | EDA: Inspect, Clean, and Validate a Dataset

an implementation of logistic regression for predicting if a person has heart Disease or not

Heart Disease Detection is a comprehensive application designed to detect the presence of heart disease based on several risk factors.

Demo machine learning project using the UCI Heart Disease dataset

A repo using machine learning to predict heart disease using NNs, Random Forest and XGBoost. A repo using machine learning to classify chest x-ray images using CNNs. The dataset is from the heart-failure-prediction dataset on Kaggle.

This is a Decision Tree Model that trained with relative dataset with hyperparameter tuning to predict whether the patient has heart disease or not with relative features

The objective of this project is to detect whether person has any chance of heart disease or not by giving number of features to person with having maximum accuracy of above 97%. By Using Machine learning algorithms and deep learning are applied to compare the results and analysis of the UCI Machine Learning Heart Disease dataset.

A Machine Learning Application which which predicts heart and liver diseases by taking attribute inputs from the user . Algorithms like SVM , Decision Tree , Linear Regressions are used .

Predictive Modeling for Cardiovascular Disease Prevention

prediction of heart disease likelihood

ML-Based Cardiac Disease Classification web application