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Investigation of An Early Prediction System of Cardiac Arrest Using Machine Learning Techniques

The increase in popularity for wearable technologies has opened the door for an Internet of Things (IoT) solution to healthcare. One of the most prevalent healthcare problems today is the poor survival rate of out-of-hospital sudden cardiac arrests. Not only that, most of the conventional device are...

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Main Author: Muhammad Afnan, Mohammad Nasir
Format: Undergraduates Project Papers
Language:English
Published: 2022
Subjects:
TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://umpir.ump.edu.my/id/eprint/39889/1/EA18069_MUHD%20AFNAN_Thesis%20-%20Muhammad%20Afnan.pdf
http://umpir.ump.edu.my/id/eprint/39889/
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spelling my.ump.umpir.398892024-01-08T03:32:12Z http://umpir.ump.edu.my/id/eprint/39889/ Investigation of An Early Prediction System of Cardiac Arrest Using Machine Learning Techniques Muhammad Afnan, Mohammad Nasir TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering The increase in popularity for wearable technologies has opened the door for an Internet of Things (IoT) solution to healthcare. One of the most prevalent healthcare problems today is the poor survival rate of out-of-hospital sudden cardiac arrests. Not only that, most of the conventional device are also wired and unfriendly device. Most of the people nowadays have no alert and better awareness about their health condition. The objective of this study is to present a multisensory system using IoT that can collect physical activity heart rates and body temperatures that can alert about their health condition. For this study, we implemented an embedded sensory system with a Low Energy Bluetooth communication module to discreetly collect electrocardiogram and body temperature data using a smartphone in a common environment. To identify approaching heart illness using Machine learning techniques, a preliminary design of a cloud-based heart disease prediction system was developed. An effective machine learning approach created from a separate examination of many machine learning algorithms in WEKA should be applied for the correct identification of heart disease. Random Forest algorithm be used in this study which is got the best performance with 83% accuracy then the other algorithms in WEKA. This algorithm was applied in the Python using Google Colab to make prediction of the sudden cardiac arrest. As the result, to make a prediction user need to set their data health in the Python using Random Forest algorithm to detect either they have a heart disease or not. 2022-06 Undergraduates Project Papers NonPeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/39889/1/EA18069_MUHD%20AFNAN_Thesis%20-%20Muhammad%20Afnan.pdf Muhammad Afnan, Mohammad Nasir (2022) Investigation of An Early Prediction System of Cardiac Arrest Using Machine Learning Techniques. College of Engineering, Universiti Malaysia Pahang Al-Sultan Abdullah.
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
Muhammad Afnan, Mohammad Nasir
Investigation of An Early Prediction System of Cardiac Arrest Using Machine Learning Techniques
description The increase in popularity for wearable technologies has opened the door for an Internet of Things (IoT) solution to healthcare. One of the most prevalent healthcare problems today is the poor survival rate of out-of-hospital sudden cardiac arrests. Not only that, most of the conventional device are also wired and unfriendly device. Most of the people nowadays have no alert and better awareness about their health condition. The objective of this study is to present a multisensory system using IoT that can collect physical activity heart rates and body temperatures that can alert about their health condition. For this study, we implemented an embedded sensory system with a Low Energy Bluetooth communication module to discreetly collect electrocardiogram and body temperature data using a smartphone in a common environment. To identify approaching heart illness using Machine learning techniques, a preliminary design of a cloud-based heart disease prediction system was developed. An effective machine learning approach created from a separate examination of many machine learning algorithms in WEKA should be applied for the correct identification of heart disease. Random Forest algorithm be used in this study which is got the best performance with 83% accuracy then the other algorithms in WEKA. This algorithm was applied in the Python using Google Colab to make prediction of the sudden cardiac arrest. As the result, to make a prediction user need to set their data health in the Python using Random Forest algorithm to detect either they have a heart disease or not.
format Undergraduates Project Papers
author Muhammad Afnan, Mohammad Nasir
author_facet Muhammad Afnan, Mohammad Nasir
author_sort Muhammad Afnan, Mohammad Nasir
title Investigation of An Early Prediction System of Cardiac Arrest Using Machine Learning Techniques
title_short Investigation of An Early Prediction System of Cardiac Arrest Using Machine Learning Techniques
title_full Investigation of An Early Prediction System of Cardiac Arrest Using Machine Learning Techniques
title_fullStr Investigation of An Early Prediction System of Cardiac Arrest Using Machine Learning Techniques
title_full_unstemmed Investigation of An Early Prediction System of Cardiac Arrest Using Machine Learning Techniques
title_sort investigation of an early prediction system of cardiac arrest using machine learning techniques
publishDate 2022
url http://umpir.ump.edu.my/id/eprint/39889/1/EA18069_MUHD%20AFNAN_Thesis%20-%20Muhammad%20Afnan.pdf
http://umpir.ump.edu.my/id/eprint/39889/
_version_ 1822924034068709376
score 13.232414

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