Machine Learning helps in predicting the Heart diseases, and the predictions made are quite accurate.

The major reason for the death in worldwide is the heart disease in high and low developed countries. The data scientist uses distinctive machine learning techniques for modeling health diseases by using authentic dataset efficiently and accurately. The medical analysts are needy for the models or systems to predict the disease in patients before the strike. High cholesterol, unhealthy diet, harmful use of alcohol, high sugar levels, high blood pressure, and smoking are the main symptoms of chances of the heart attack in humans. Data Science is an advanced and enhanced method for the analysis and encapsulation of useful information. The attributes and variable in the dataset discover an unknown and future state of the model using prediction in machine learning. Chest pain, blood pressure, cholesterol, blood sugar, family history of heart disease, obesity, and physical inactivity are the chances that influence the possibility of heart diseases. This project emphasizes to evaluate different algorithms for the diagnosis of heart disease with better accuracies by using the patient’s data set because predictions and descriptions are fundamental objectives of machine learning. Each procedure has unique perspective for the modeling objectives. Algorithms have been implemented for the prediction of heart disease with our Heart patient data set

Multiple disease prediction such as Diabetes, Heart disease, Kidney disease, Breast cancer, Liver disease, Malaria, and Pneumonia using supervised machine learning and deep learning algorithms.

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Heart Disease Prediction System using Machine Learning

This repository implements the Heart Disease Prediction using various machine learning approaches

Intelligent Python service with FastAPI for real-time heart disease predictions using machine learning. Features AI-assisted consultations, user authentication, analysis history, RESTful API, and comprehensive error handling. Secure and scalable solution for healthcare applications.

Heart disease prediction system Project using Machine Learning with Code and Report

This repo contains the code for a machine learning based prediction system where the prediction of heart disease can be done using ML techniques and several classifiers have been compared.

Heart disease prediction using retina images leverages advanced imaging and machine learning techniques to assess cardiovascular risk. The retina, being a highly vascularized structure, provides crucial insights into the condition of blood vessels, which are often affected in heart disease.

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Heart disease prediction using retina images leverages advanced imaging and machine learning techniques to assess cardiovascular risk. The retina, being a highly vascularized structure, provides crucial insights into the condition of blood vessels, which are often affected in heart disease

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Creation of a Web application using R and Shiny for prediction of Heart Disease using Machine Learning

PredictiX is a comprehensive multi-disease prediction system built using the MERN stack and integrated with machine learning models. It accurately predicts lung cancer, breast cancer, diabetes, and heart disease, providing a seamless user experience for health diagnostics.

The Heart Disease Prediction project aims to predict the likelihood of heart disease using machine learning techniques.

Final year project (Heart disease prediction using machine learning techniques)

A machine learning web application used to depict presence of heart disease, made using Random Forest Classifier and Flask. Deployed on pythonanywhere

Heart disease prediction with logistic regression using SAS Studio. The dataset is taken from UCI Machine Learning about heart disease.

An ensemble machine learning model based on quantum ‎machine learning ‎classifiers is proposed to predict the risk of heart disease. The proposed ‎model ‎is a bagging ensemble learning model where Quantum ‎Support Vector ‎Classifier is used as the base classifier. Furthermore, in order to make the ‎model's outcomes more explainable, the importance of every single feature in ‎the prediction is computed and visualized using SHapley Additive exPlanations ‎‎(SHAP) framework. In the experimental study, other stand-alone quantum ‎classifiers, namely, ‎Quantum Support Vector Classifier (QSVC‎),‎ Quantum ‎Neural Network ‎‎(QNN), and Variational ‎‎Quantum Classifier ‎(VQC) were ‎applied and compared with classical machine learning classifiers ‎such as ‎Support Vector ‎Classifier ‎(SVC), and Artificial Neural Network ‎(ANN). ‎

Python Jupiter Notebook

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"Coding a Streamlit web app for heart disease prediction using a trained machine learning model."

Heart Disease Risk Prediction using Machine Learning

Heart Disease Prediction using Machine Learning | Tools Used: Jupyter Notebook, Spyder, Weka, RapidMiner | Models: Naive Bayes, Decision Tree, AdaBoost, Ensembling Method

Used supervised machine-learning algorithms

Utilized machine learning techniques to predict prices associated with heart disease. Leverage advanced algorithms, feature engineering, and model optimization to provide accurate predictions

Heart Disease Prediction using machine and deep learning techniques works on heart dataset

A variety of conditions that affect your heart are referred to as heart disease. According to World Health Organization reports, cardiovascular diseases are now the leading cause of death worldwide, with 17.9 million deaths per year. Artificial intelligence and machine learning are now widely acknowledged to play an important role in the medical field, where they are used to diagnose diseases, classify or forecast outcomes using a variety of machine learning and deep learning models. Machine learning algorithms can quickly adapt to a thorough analysis of genetic data. For accurate estimation, medical records can be changed and studied more thoroughly, and better models can be identified for accurate prediction. Using a different algorithm, several researchers have reported on the prediction of heart problems.The aim of this study is to diagnose heart disease using machine learning algorithms. Machine Learning can help predict the presence or absence of locomotor disorders, heart diseases, and other conditions. Artificial intelligence (AI) has the potential to solve this problem right now. To improve the classification accuracy of a heart disease data set, we propose combining KNN, logistics regression, SVM, Random Forest algorithm, and decision tree algorithm. The proposed approach was applied to the dataset, which included first a thorough analysis of the data, followed by the use of various machine learning algorithms, including linear model selection and Logistic Regression. KNeighborsClassifier was used to focus on neighbour selection, followed by a tree-based technique like DecisionTreeClassifier, and finally a very popular and most popular ensemble method RandomForestClassifier. Support Vector Machine was also used to check and handle the data's high dimensionality.

HeartGuard AI is a machine learning-based heart disease prediction system built using Support Vector Machine (SVM). It provides real-time risk prediction, probability scoring, confusion matrix, ROC curve visualization, and performance metrics through an interactive Streamlit web interface.