ENSEMBLE LEARNING ALGORITHM BASED HEART DISEASE PREDICTION USING INTERNET OF THINGS IMPLEMENTATION

Authors

  • Yasmeen Shaikh*, Vasudev Parvati & Sangappa Ramachandra Biradar

Keywords:

Ensemble Learning algorithm, Extended Isolation Forest, Thingspeak Cloud, Heart disease prediction.

Abstract

Internet of Things based health monitoring is getting importance with improvement in medical field for a decade now. Machine Learning algorithms are deployed in the cloud environment for prediction of diseases with wearable sensors getting importance in the medical applications. This paper attempts to develop the IOT based heart disease prediction algorithm using Ensemble learning algorithms. Sensors related to heart disease prediction is incorporated in the implementation that collects the data from the patients and collected in the cloud environment. Since applied in the academic purpose Thingspeak cloud environment is used for the cloud environment. Extended Isolation Forest is used anomaly detection in the data preprocessing stage and different sampling techniques for imbalance data. Ensemble Learning algorithm combines classifiers Multilayer perceptron, Decision Tree, Support Vector Classifier to form the stack. Classification accuracy for each algorithm is compared with the Ensemble Learning algorithm which is the stack of all the above algorithms.

Published

2022-09-05

How to Cite

Yasmeen Shaikh*, Vasudev Parvati & Sangappa Ramachandra Biradar. (2022). ENSEMBLE LEARNING ALGORITHM BASED HEART DISEASE PREDICTION USING INTERNET OF THINGS IMPLEMENTATION. Harbin Gongye Daxue Xuebao/Journal of Harbin Institute of Technology, 54(9), 46–57. Retrieved from http://hebgydxxb.periodicales.com/index.php/JHIT/article/view/1299

Issue

Section

Articles