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

Flask based web app with five machine learning models on the 10 most common disease prediction, covid19 prediction, breast cancer, chronic kidney disease and heart disease predictions with their symptoms as inputs or medical report (pdf format) as input.

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.

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). ‎

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

Heart Disease Prediction App This is a machine learning-powered web app designed to help users estimate their risk of heart disease based on health parameters. It integrates data science, predictive modeling, and intuitive UI/UX to deliver insights in minutes.

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

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

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.

This project is a Streamlit-based web application designed to predict the likelihood of various diseases based on user-provided health data. By leveraging machine learning models, the app offers predictions for conditions such as diabetes, heart disease, Parkinson's disease, lung cancer, and hypothyroidism.

A machine learning tool that predicts the likelihood of cancer and heart disease using advanced classification models. The repository includes features for data preprocessing, hyperparameter tuning, batch predictions, and model evaluation, aimed at enhancing early diagnosis and health insights.

A Heart Disease Prediction project leveraging Machine Learning algorithms to analyze medical data, build predictive models, and identify factors contributing to heart disease, ensuring accurate and insightful outcomes.

🤖This is an interactive Streamlit web application that predicts the likelihood of multiple diseases(Diabetes Prediction, Heart Disease Prediction, Parkinson's Disease Prediction) using Machine Learning models.

This repository demonstrates the project of "Heart Disease Prediction using Machine Learning". This project has been created by implementing the K Nearest Neighbors Algorithm. Initially, the Machine Learning model of KNN Algorithm is trained 67% using heart_disease_train dataset and later on the expected results are tested and obtained successfully with 33% of dataset used for the testing purposes. The accuracy of around 85.06 % was achieved after the successful execution of the Machine Learning Model.

HealthOrzo is a Disease Prediction and Information Website. It is user friendly and very dynamic in it's prediction. The Project Predicts 4 diseases that are Diabetes , Kidney Disease , Heart Ailment and Liver Disease . All these 4 Machine Learning Models are integrated in a website using Flask at the backend .

Multiple Disease Prediction System using Machine Learning: This project provides a streamlit web application for predicting multiple diseases, including diabetes, Parkinson's disease, and heart disease, using machine learning algorithms. The prediction models are deployed using Streamlit, a Python library for building interactive web applications.

This project aims to predict heart disease using four machine learning algorithms: Logistic Regression, Random Forest Classifier, K-Neighbors Classifier, and Decision Tree Classifier. By comparing their accuracies, we identify the most effective model for heart disease prediction.

Heart disease is a major global health concern that affects millions of people around the world. Early detection and accurate prediction of heart disease can help to prevent the progression of the disease and save lives. In this project, we aim to develop a predictive model for heart disease using various machine learning algorithms.

This project is a Flask-based web application that predicts the likelihood of three medical conditions: Diabetes, Heart Disease, and Parkinson's Disease. It uses machine learning models to make predictions based on user-provided health parameters.

Introduction In my case studies I keep writing in English because it is used in Kaggle and I also keep them in Portuguese because my native language is Brazilian Portuguese, so we can share more knowledge and experiences in Kaggle with our Brazilian colleagues. We will develop and analyze the algorithms with the best capacity and identify the problems in the heart and at the end we will make a comparison between them. Description Context Cardiovascular diseases (CVDs) are the number 1 cause of death globally, taking an estimated 17.9 million lives each year, which accounts for 31% of all deaths worldwide. Four out of 5CVD deaths are due to heart attacks and strokes, and one-third of these deaths occur prematurely in people under 70 years of age. Heart failure is a common event caused by CVDs and this dataset contains 11 features that can be used to predict a possible heart disease. People with cardiovascular disease or who are at high cardiovascular risk (due to the presence of one or more risk factors such as hypertension, diabetes, hyperlipidaemia or already established disease) need early detection and management wherein a machine learning model can be of great help. Attribute Information Age: age of the patient [years] Sex: sex of the patient [M: Male, F: Female] ChestPainType: chest pain type [TA: Typical Angina, ATA: Atypical Angina, NAP: Non-Anginal Pain, ASY: Asymptomatic] RestingBP: resting blood pressure [mm Hg] Cholesterol: serum cholesterol [mm/dl] FastingBS: fasting blood sugar [1: if FastingBS > 120 mg/dl, 0: otherwise] RestingECG: resting electrocardiogram results [Normal: Normal, ST: having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV), LVH: showing probable or definite left ventricular hypertrophy by Estes' criteria] MaxHR: maximum heart rate achieved [Numeric value between 60 and 202] ExerciseAngina: exercise-induced angina [Y: Yes, N: No] Oldpeak: oldpeak = ST [Numeric value measured in depression] ST_Slope: the slope of the peak exercise ST segment [Up: upsloping, Flat: flat, Down: downsloping] HeartDisease: output class [1: heart disease, 0: Normal] Source This dataset was created by combining different datasets already available independently but not combined before. In this dataset, 5 heart datasets are combined over 11 common features which makes it the largest heart disease dataset available so far for research purposes. The five datasets used for its curation are: Cleveland: 303 observations Hungarian: 294 observations Switzerland: 123 observations Long Beach VA: 200 observations Stalog (Heart) Data Set: 270 observations Total: 1190 observations Duplicated: 272 observations Final dataset: 918 observations Every dataset used can be found under the Index of heart disease datasets from UCI Machine Learning Repository on the following link: https://archive.ics.uci.edu/ml/machine-learning-databases/heart-disease/ Citation fedesoriano. (September 2021). Heart Failure Prediction Dataset. Retrieved [Date Retrieved] from https://www.kaggle.com/fedesoriano/heart-failure-prediction. Acknowledgements Creators: Hungarian Institute of Cardiology. Budapest: Andras Janosi, M.D. University Hospital, Zurich, Switzerland: William Steinbrunn, M.D. University Hospital, Basel, Switzerland: Matthias Pfisterer, M.D. V.A. Medical Center, Long Beach and Cleveland Clinic Foundation: Robert Detrano, M.D., Ph.D. Donor: David W. Aha (aha '@' ics.uci.edu) (714) 856-8779

This project aims to predict the presence of heart disease in patients based on various attributes such as age, sex, chest pain type, blood pressure, cholesterol level, etc. The project uses three different ML & DL models. The project can be direct run on google colab after uploading the dataset to the notebook in colab.

⭐ A Machine Learning-based app for heart disease prediction using classification models, featuring real-time predictions via Streamlit.

Heart | Covid-19| ChronicKidney Diseases Prediction site with Machine Learning model(Random Forest Regressor) ,Which has given best accuracy among the machine learning classification models.

SmartHeart is an AI-powered heart disease prediction system built with Python and machine learning techniques, comparing multiple supervised models to support early and accurate medical diagnosis.

Developed a comprehensive web application using Streamlit, designed to perform four medical tests: brain tumor classification, diabetes prediction, heart disease prediction, and Parkinson's disease prediction. Trained and deployed four separate machine learning models for each medical test using frameworks such as TensorFlow and Scikit-Learn

A FastAPI-powered REST API that serves predictions from a machine learning model trained to detect heart disease. This project focuses on containerization with Docker and deployment to the cloud using Render. Built as part of a hands-on assignment to demonstrate practical DevOps, ML, and API development skills.

Heart disease is one of the world's and our country's leading causes of death. Heart disease is caused by diabetes, genetics, high blood pressure and high cholesterol. The majority of the time, heart diseases occur without causing any symptoms. These circumstances can result in major health issues and even death. I will provide a valuable application in the field of preserving public health with this estimating program by enabling early identification of heart disease, which is the most common today and whose symptoms are quite variable. I aimed to prevent possible bad results with early diagnosis. The users are doctors and potential patients with the necessary health data for prediction. The dataset I use in the project is the Heart Disease dataset I got from the UCI Machine Learning Repository. Although there are 76 attributes in this dataset, all published studies only use a subset of 14 of them and dataset consist of 303 rows. The input of the project is the health data we receive from the user in line with the features in dataset. The output of the project is the result of either there is a risk of heart disease or there is no risk of heart disease, which will be calculated and returned according to the data entered by the user as a result of all operations. The machine learning model I chose to use is Linear SVM. While choosing the most suitable algorithm for the project, I considered the training period, the number of features, the output of the project, the number of columns and parameters in the dataset, and the inputs and output of the project. In this way, I tried many suitable algorithms and decided to use the Linear SVM (Support Vector Machine) algorithm with the highest performance and accuracy percentage. Using this algorithm, I achieved almost 86% accuracy. I found this algorithm suitable for the project, because SVM is a classification algorithm. It tries to find the best line called hyperplane separating the two classes. The algorithm ensures that the line to be drawn is set to pass from the farthest place to its elements in two classes. In the project there are 2 classes, those at risk of heart disease and those without. Linear SVM is used for linearly separable data like in the Project. Lastly, I learned the tkinter library and coded the GUI to bring these codes to the user and to create my application.

A Machine Learning model to predict Heart Disease Prediction.

This project compares four machine-learning models (LightGBM, XGBoost, Random Classifier, Logistic Regression) on a dataset with 18 attributes and 320,000 instances for heart disease prediction. The study aims to provide insights for developing accurate Heart Disease prediction models.

A machine learning project on heart disease prediction using UCI dataset, and deploying a web-app for the machine learning model using Flask.