Cardiovascular Disease Prediction Using Hybrid-Random-Forest- Linear- Model (HRFLM)
Abbaraju Sai Sathwik, Beebi Naseeba, Nagendra Panini Challa
Abstract
Heart complications has become very common disease among all the age group persons across the globe. The computation techniques available in the market are based different traditional machine learning models. These models are successful based on the type of datasets they have adapted. In this work two models are combined to form a hybrid model (HRFLM) which is suitable for cardiovascular risk prediction. This model utilized different attributes like stress levels, ECG data and others which are ideal for cardiovascular risk prediction. The stress levels are considered as key attribute which is vital for evaluating this hybrid model. The results show that the proposed model has obtained 98.36% accuracy in predicting the cardiovascular disease when compared with other traditional models.