Treatment Of Heart Disease Patients Research Articles

PHYSIOVR: FERRAMENTA DE REALIDADE VIRTUAL APLICADO NA REABILITAÇÃO CARDIOVASCULAR

Cardiovascular diseases (CVD) are the main causes of death in the world and many people who have CVD survive, making intervention measures necessary that are effective in their cardiovascular rehabilitation (CVR). This article presents the PHYSIOVR computational tool, which uses Virtual Reality (VR), to assist in the treatment of heart disease patients during physical activities, using customized 3D virtual environments, aiming to improve functional capacity and cognitive engagement. The tool was evaluated through a usability questionnaire, obtaining a final average of 4.6 out of 5.0 points, and then it was evaluated by physical therapists, obtaining an overall satisfaction level of 9.1 ± 0.73.

Performance Analysis by Using the Knime Analytical Platform to Forecast Heart Failure Using Several Machine Learning Methods

Abstract: Using a privately available dataset from kaggle.com, this research compares the performance of six well-known machine-learning approaches for predicting heart failure. which include Logistic Regression, Gradient Boosted Trees (GBT), Naive Bayes, Random Forest (RF), and Tree Ensemble. Heart failure is a major public health problem and it is necessary to improve the treatment of heart disease patients to increase the rate of survival. Delicacy was used to assess the performance of machine learning methods. RF produced the highest performance score of 80% when compared to Decision Tree Classifier and Tree Ensemble, Gradient BoostedTrees (GBT), Naive Bayes, and Logistic Regressions.