Hierarchical Random Forest Formation with Nonlinear Regression Model for Cardiovascular Diseases Prediction

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.