No description available

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). ‎

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

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.

Multiple Disease Prediction System using Machine Learning. Predicts Parkinson's, Heart Disease, and Diabetes via a web interface powered by Logistic Regression, SVM, KNN, and Stacking Ensemble. Designed for early diagnosis and healthcare support.

The prediction of heart disease is performed using Ensemble of machine learning algorithms. This is to boost the accuracy achieved by individual machine learning algorithms. Dataset used: https://www.kaggle.com/ronitf/heart-disease-uci

## Project Demo URL https://diabetiesproject.azurewebsites.net •This repository consists of files required to deploy a **Web App** created with **Flask on Microsoft Azure**.# diabetes_predictor The project helps the user to identify whether someone is suffering from diabetes by simply inputting certain values like BMI, Glucose level, Blood pressure etc. with the help of a Kaggle database. By using the statistical data about how certain aspects like BMI, Glucose level, Insulin level, age etc. impact if an individual is prone to diabetes or not, the project will be able to tell the user if the person has diabetes or not by entering those values. So in a way the project will help in monitoring the likelihood of someone developing diabetes. The project can be extended to include other diseases prediction which I will incorporate later down the road. ### Problem Statement/Oppurtunity: Diabetes is an illness caused because of high glucose level in a human body. Diabetes should not be ignored if it is untreated then Diabetes may cause some major issues in a person like: heart related problems, kidney problem, blood pressure, eye damage and it can also affects other organs of human body. Diabetes can be controlled if it is predicted earlier. To achieve this goal this project work we will do early prediction of Diabetes in a human body or a patient for a higher accuracy through applying, Various Machine Learning Techniques. ## Project Discription: Machine learning techniques Provide better result for prediction by con- structing models from datasets collected from patients. In this work we will use Machine Learning Classification and ensemble techniques on a dataset to predict diabetes. Which are K-Nearest Neighbor (KNN), Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM), Gradient Boosting (GB) and Random Forest (RF). The accuracy is different for every model when compared to other models. The Project work gives the accurate or higher accuracy model shows that the model is capa- ble of predicting diabetes effectively. Our Result shows that Random Forest achieved higher accuracy compared to other machine learning techniques. The is a Microsoft Azure Web App project that helps the user to identify whether someone is suffering from diabetes by simply inputting certain values like BMI, Glucose level, Blood pressure etc. with the help of Kaggle database. ## Primary Ajure Technology: **Azure Web Apps , AI+Machine Learning, Computer Vision, Static Web Apps, Web Apps** ## Conclusion: Hence we succesfully predict that any person having diabities or not. Thank You # f r t p r o j e c t # f r t p r o j e c t

A machine learning project for heart disease prediction using the Cleveland dataset, featuring ensemble models (Random Forest, AdaBoost, XGBoost) and an interactive Streamlit web app for diagnosis.

Project Sushruta develops a heart disease prediction model with a 81% accuracy using advanced Machine Learning and Deep Learning techniques. Feature selection is automated with RFE and PCA, and the model is trained using Random Forest, Ensemble Learning (stacking), and a hybrid ANN-Random Forest approach for enhanced performance.

No description available

Heart Disease Prediction Using Machine Learning Ensembles

No description available

Heart Disease prediction using Ensemble Machine Learning

No description available

A machine learning-based project designed to predict the likelihood of heart disease in individuals. The system utilizes advanced algorithms to analyze patient data and provide insights into their heart health status.

Heart disease remains one of the leading causes of mortality globally. This study applies multiple machine learning and ensemble learning techniques to predict heart disease using clinical and lifestyle factors from the CDC BRFSS 2015 dataset.

No description available

No description available

Engineered a predictive model for heart disease and stroke, employing an ensemble machine learning algorithm. By harnessing diverse model strengths, the system delivers accurate predictions grounded in various health parameters, elevating precision in cardiovascular risk assessment.

Heart disease prediction using advanced machine learning and ensemble methods. Combines models like Random Forest, XGBoost, and SVM with stacking and voting techniques for improved accuracy. Includes data preprocessing, EDA, and performance evaluation.

Prediction of heart disease risk using ensemble machine learning

Using a Machine Learning Ensemble to Make Heart Disease Prediction in R

Heart Disease Prediction performed using Ensemble of machine learning algorithms.

Machine Learning–based prediction of heart disease using ensemble classification models.

Heart disease prediction using machine learning and ensemble models (UCI/Kaggle dataset)

Machine learning project for heart disease prediction using stacking ensemble and model comparison.

AI-powered heart disease risk prediction system using ensemble machine learning models (Flask + scikit-learn)

Machine Learning Web App for Diabetes and Heart Disease Risk Prediction using Ensemble Models and Neural Networks

Machine learning project on cardiovascular disease prediction (heart disease prediction). The project uses 4 algorithms, Logistic Regression, Decision Trees, Random Forests and Ensemble Learning.

Interpretable machine learning pipeline for heart disease prediction using tree-based models, ensemble learning, SHAP explanations, LIME, and counterfactual analysis (DiCE).