A Fog-Enabled Framework for Ensemble Machine Learning-Based Real-Time Heart Patient Diagnosis

A Fog-Enabled Framework for Ensemble Machine Learning-Based Real-Time Heart Patient Diagnosis
  IJETT-book-cover           
  
© 2023 by IJETT Journal
Volume-71 Issue-8
Year of Publication : 2023
Author : Mohammed S Atoum, Abhilash Pati, Manoranjan Parhi, Binod Kumar Pattanayak, Ahmad Khader Habboush, Mohammad Alnabhan, Emad Qalaja
DOI : 10.14445/22315381/IJETT-V71I8P204

How to Cite?

Mohammed S Atoum, Abhilash Pati, Manoranjan Parhi, Binod Kumar Pattanayak, Ahmad Khader Habboush, Mohammad Alnabhan, Emad Qalaja, "A Fog-Enabled Framework for Ensemble Machine Learning-Based Real-Time Heart Patient Diagnosis," International Journal of Engineering Trends and Technology, vol. 71, no. 8, pp. 39-47, 2023. Crossref, https://doi.org/10.14445/22315381/IJETT-V71I8P204

Abstract
Accurately forecasting human diseases continues to be a challenging issue in the search for better and more crucial studies. Heart disease is a potentially deadly condition that affects individuals everywhere. With the use of data fusion techniques and adaptive machine learning (ML) methods on diverse medical management datasets, the Internet of Medical Things (IoMT) plays a crucial role. A healthcare monitoring suggestion system accurately identifies and proposes heart patient issues. Various machine-learning approaches and algorithms for predicting cardiac disorders have recently been created. Many systems cannot handle the massive volume of multi-feature raw data on cardiac disorders. In this work, the two datasets on heart diseases from the UCI-ML warehouse, namely, Cleveland and Hungarian, are considered and then fused into a single dataset along with five basic ML classifiers. The suggested approach attained the maximum level of accuracy considering stacking as an ensemble classifier on these five ML approaches, particularly on the fused dataset, against these five classifiers. The experiments' accuracy, precision, sensitivity, specificity, f-measure, and MCC reached 82.39%, 85.86%, 83.33%, 81.08%, 84.58%, and 64.09%, which are comparatively higher. This study uses Fog computing ideas and can remotely diagnose cardiac patients instantly in low latency, minimum energy consumption, etc., as from the experiments.

Keywords
Fog computing, IoMT, Ensemble learning, Machine Learning, Heart Diseases Diagnosis.

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