Healthcare Monitoring using Machine Learning Based Data Analytics

Abstract

In this paper, we develop a machine learning based healthcare monitoring and analytics from various Internet of Medical Things (IoMT) devices for possible prediction of cardiovascular risk in patients. The study uses random forest for feature selection and then the fuzzy logic classifier is used for prediction of Cardio Vascular Disease (CVD). The simulation is conducted to test the efficacy of the proposed machine learning based data analytics model over various other methods. The results show than the proposed method has higher rate of classification accuracy in classifying the CVD with higher recall and F1-score than other methods.

Random Forest-Based Prediction Models for Assessing Cardiovascular Disease Risk: Integrating Clinical and Genetic Factors

“…Gradient Boosting is an outfit learning strategy where frail learners (typically decision trees) are successively included to the show, with each unused learner redressing blunders made by the past ones [8]. It minimizes a misfortune work (e.g., cruel squared mistake for relapse, calculated misfortune for classification) by angle descent.…”