This project will focus on predicting heart disease using neural networks. Based on attributes such as blood pressure, cholestoral levels, heart rate, and other characteristic attributes, patients will be classified according to varying degrees of coronary artery disease

Analysis and modeling of the ventricles and myocardium are important in the diagnostic and treatment of heart diseases. Manual delineation of those tissues in cardiac MR (CMR) scans is laborious and time-consuming. The ambiguity of the boundaries makes the segmentation task rather challenging. Furthermore, the annotations on some modalities such as Late Gadolinium Enhancement (LGE) MRI, are often not available. We propose an end-to-end segmentation framework based on convolutional neural network (CNN) and adversarial learning. A dilated residual U-shape network is used as a segmentor to generate the prediction mask; meanwhile, a CNN is utilized as a discriminator model to judge the segmentation quality. To leverage the available annotations across modalities per patient, a new loss function named weak domain-transfer loss is introduced to the pipeline. The proposed model is evaluated on the public dataset released by the challenge organizer in MICCAI 2019, which consists of 45 sets of multi-sequence CMR images. We demonstrate that the proposed adversarial pipeline outperforms baseline deep-learning methods.

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). ‎

This project will focus on predicting heart disease using neural networks. Based on attributes such as blood pressure, cholestoral levels, heart rate, and other characteristic attributes, patients will be classified according to varying degrees of coronary artery disease. This project will utilize a dataset of 303 patients and distributed by the UCI Machine Learning Repository. Machine learning and artificial intelligence is going to have a dramatic impact on the health field; as a result, familiarizing yourself with the data processing techniques appropriate for numerical health data and the most widely used algorithms for classification tasks is an incredibly valuable use of your time! In this tutorial, we will do exactly that. We will be using some common Python libraries, such as pandas, numpy, and matplotlib. Furthermore, for the machine learning side of this project, we will be using sklearn and keras.

Pollutants in the atmosphere is a major problem in metropolitan cities due to vehicle and factory pollution. There is a need to take preliminary measures to overcome this problem since it leads to many health related issues like asthma and heart problems. So, there is a need to predict the level of these toxicants during various times of the day in order to warn the people suffering from these diseases so that they can be more cautious during the critical intervals of the day. We have identified the major pollutants of the atmosphere as NO2 and O3 along with 11 other pollutants which are either meteorological or geographic and particulate concentrations. We have used the techniques for prediction such as Artificial Neural Networks(ANN) and ANFIS(Adaptive Neuro Fuzzy Inference Systems) and compared them to find out that one perfect method that can precisely predict the concentrations for every 8 hours of the day.

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Heart Disease Prediction using Deep Learning This project applies deep learning techniques to predict the likelihood of heart disease based on clinical and health-related features. Using the popular UCI Heart Disease dataset, a neural network model is trained to classify patients as having heart disease or not.

Heart disease prediction system using machine learning with 95% accuracy. Compares 8 algorithms including Random Forest and Neural Networks. Analyzes 13 medical attributes to provide early risk assessment for healthcare professionals, enabling timely intervention and potentially saving lives."

Heart Disease prediction using a Convolutional Neural Network

Prediction on 10 year risk of coronary heart disease (CHD), using Neural Network & Classification Models

Heart Disease Prediction using Logistic Regression, Naive Bayes, Support Vector Machine, K-Nearest Neighbors, Decision Tree, Random Forest, XGBoost, Artificial Neural Network

Heart-Disease-Prediction-using-Machine-Learning Thus preventing Heart diseases has become more than necessary. Good data-driven systems for predicting heart diseases can improve the entire research and prevention process, making sure that more people can live healthy lives. This is where Machine Learning comes into play. Machine Learning helps in predicting the Heart diseases, and the predictions made are quite accurate. The project involved analysis of the heart disease patient dataset with proper data processing. Then, different models were trained and and predictions are made with different algorithms KNN, Decision Tree, Random Forest,SVM,Logistic Regression etc This is the jupyter notebook code and dataset I've used for my Kaggle kernel 'Binary Classification with Sklearn and Keras' I've used a variety of Machine Learning algorithms, implemented in Python, to predict the presence of heart disease in a patient. This is a classification problem, with input features as a variety of parameters, and the target variable as a binary variable, predicting whether heart disease is present or not. Machine Learning algorithms used: Logistic Regression (Scikit-learn) Naive Bayes (Scikit-learn) Support Vector Machine (Linear) (Scikit-learn) K-Nearest Neighbours (Scikit-learn) Decision Tree (Scikit-learn) Random Forest (Scikit-learn) XGBoost (Scikit-learn) Artificial Neural Network with 1 Hidden layer (Keras) Accuracy achieved: 95% (Random Forest) Dataset used: https://www.kaggle.com/ronitf/heart-disease-uci

Heart-Disease-Prediction-using-Machine-Learning Thus preventing Heart diseases has become more than necessary. Good data-driven systems for predicting heart diseases can improve the entire research and prevention process, making sure that more people can live healthy lives. This is where Machine Learning comes into play. Machine Learning helps in predicting the Heart diseases, and the predictions made are quite accurate. The project involved analysis of the heart disease patient dataset with proper data processing. Then, different models were trained and and predictions are made with different algorithms KNN, Decision Tree, Random Forest,SVM,Logistic Regression etc This is the jupyter notebook code and dataset I've used for my Kaggle kernel 'Binary Classification with Sklearn and Keras' I've used a variety of Machine Learning algorithms, implemented in Python, to predict the presence of heart disease in a patient. This is a classification problem, with input features as a variety of parameters, and the target variable as a binary variable, predicting whether heart disease is present or not. Machine Learning algorithms used: Logistic Regression (Scikit-learn) Naive Bayes (Scikit-learn) Support Vector Machine (Linear) (Scikit-learn) K-Nearest Neighbours (Scikit-learn) Decision Tree (Scikit-learn) Random Forest (Scikit-learn) XGBoost (Scikit-learn) Artificial Neural Network with 1 Hidden layer (Keras) Accuracy achieved: 95% (Random Forest) Dataset used: https://www.kaggle.com/ronitf/heart-disease-uci

Heart disease is considered as one of the major causes of death throughout the world. It cannot be easily predicted by the medical practitioners as it is a difficult task which demands expertise and higher knowledge for prediction. An automated system in medical diagnosis would enhance medical efficiency and also reduce costs. We will design a system that can efficiently discover the rules to predict the risk level of patients based on the given parameters about their health. The project undertaken is to apply four machine learning models on datasets obtained from the UCI machine learning repository. The goal is to extract hidden patterns by applying data mining techniques, which are noteworthy to heart diseases and to predict the presence of heart disease in patients where the presence is valued on a scale. We are using four machine learning algorithms namely Decision Tree, Support Vector Machine (SVM), Logistic Regression, Neural Network. Our objective is to find out the suitable machine learning technique that is computationally efficient as well as accurate for the prediction of heart disease and thereafter recommending the diet one should consume and exercise one should practice in order to lower the predicted rate. Item Based Collaborative Filtering is used as the algorithm for the recommendation system. Item-based collaborative filtering is a model-based algorithm for making recommendations.

Thus preventing Heart diseases has become more than necessary. Good data-driven systems for predicting heart diseases can improve the entire research and prevention process, making sure that more people can live healthy lives. This is where Machine Learning comes into play. Machine Learning helps in predicting the Heart diseases, and the predictions made are quite accurate. The project involved analysis of the heart disease patient dataset with proper data processing. Then, different models were trained and and predictions are made with different algorithms KNN, Decision Tree, Random Forest,SVM,Logistic Regression etc This is the jupyter notebook code and dataset I've used for my Kaggle kernel 'Binary Classification with Sklearn and Keras' I've used a variety of Machine Learning algorithms, implemented in Python, to predict the presence of heart disease in a patient. This is a classification problem, with input features as a variety of parameters, and the target variable as a binary variable, predicting whether heart disease is present or not. Machine Learning algorithms used: Logistic Regression (Scikit-learn) Naive Bayes (Scikit-learn) Support Vector Machine (Linear) (Scikit-learn) K-Nearest Neighbours (Scikit-learn) Decision Tree (Scikit-learn) Random Forest (Scikit-learn) XGBoost (Scikit-learn) Artificial Neural Network with 1 Hidden layer (Keras) Accuracy achieved: 95% (Random Forest)

This project aims to predict the presence of heart disease in individuals using a **Neural Network** model.

Heart Disease Prediction using Neural Networks

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A Heart Disease Prediction using Neural Networks

Heart disease prediction using neural network algorithm

Heart Disease Prediction using Neural networks and ML Algorithms

Heart Disease Prediction using Neural Networks based upon the Kaggle Dataset on Heart Disease UCI.

Heart Disease Classification and Prediction using Artificial Neural Network (ANN)

Heart Disease Prediction Using Neural Network and It's Comparison with XGBoost

Heart disease prediction using Random Forest, Gradient Boosting, Support vector machines and artificial neural networks

Heart disease prediction using different machine learning algorithms (SVM, KNN, Logistic Regression, Decision Trees, Neural Networks)

Enhancing Heart Disease Prediction Using Neural Networks and Explainable AI for Personalized Risk Assessment in Healthcare.

Deep learning-based heart disease prediction model using neural networks. Implements data pre-processing, feature selection, and classification for accurate diagnosis.

Deep learning–based heart disease prediction system using neural networks, with focus on medical-safe threshold tuning, confusion matrix analysis, and ROC–AUC evaluation.

This Heart Disease Prediction App uses an Artificial Neural Network (ANN) model to instantly assess a patient's risk of heart disease, based on 13 key clinical and demographic health parameters.