The proposed Hierarchical Random Forest Formation with Nonlinear Regression Model (HRFFNRM) achieved a 90.3% accuracy in predicting cardiovascular diseases using ICU patient data.
Does the Hierarchical Random Forest Formation with Nonlinear Regression Model (HRFFNRM) accurately predict cardiovascular diseases in ICU patients?
A novel machine learning model (HRFFNRM) achieved 90.3% accuracy in predicting cardiovascular diseases using ICU patient data.
Cardiovascular diseases (CVD) like stroke and heart attack have a higher risk to cause the life of human beings in heart diseases category. The challenge to predict cardiovascular diseases is high risky by using the hospitalized patients Intensive Care Unit (ICU) data. The recent technologies play a vital role in the medical industry to predict various health causes using various algorithms. Machine Learning (ML) technologies are one among them to predict the diseases using quality dataset and data analysis model. In this paper, we propose a model to find the similarity by using Hierarchical Random Forest Formation with Nonlinear Regression Model (HRFFNRM). By using this model, which produces 90.3% accurate prediction in cardiovascular diseases.
Aslam et al. (Wed,) conducted a other in Cardiovascular diseases. Hierarchical Random Forest Formation with Nonlinear Regression Model (HRFFNRM) was evaluated on Prediction accuracy. The proposed Hierarchical Random Forest Formation with Nonlinear Regression Model (HRFFNRM) achieved a 90.3% accuracy in predicting cardiovascular diseases using ICU patient data.