Cover Image

A Review on Predictive Model for Heart Disease using Wearable Devices Datasets

Heart diseases were the number one killer in Malaysia based on the data from the Department of Statistics Malaysia in the previous year. Heart diseases were the principal causes of death for the population aged 41 and above. Many studies have discovered the factors that cause heart disease and ways...

Full description

Saved in:
Bibliographic Details
Main Authors: Mohd Syafiq Asyraf, Suhaimi, Nor Azuana, Ramli, Noryanti, Muhammad
Format: Article
Language:English
Published: Penerbit UniMAP 2024
Subjects:
QA Mathematics
QA75 Electronic computers. Computer science
Online Access:http://umpir.ump.edu.my/id/eprint/41455/1/100-112%2BA%2BReview%2Bon%2BPredictive%2BModel%2Bfor%2BHeart%2BDisease%2Busing%2BWearable%2BDevices%2BDatasets.pdf
http://umpir.ump.edu.my/id/eprint/41455/
https://ejournal.unimap.edu.my/index.php/amci/article/view/367
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.ump.umpir.41455
record_format eprints
spelling my.ump.umpir.414552024-06-05T04:02:49Z http://umpir.ump.edu.my/id/eprint/41455/ A Review on Predictive Model for Heart Disease using Wearable Devices Datasets Mohd Syafiq Asyraf, Suhaimi Nor Azuana, Ramli Noryanti, Muhammad QA Mathematics QA75 Electronic computers. Computer science Heart diseases were the number one killer in Malaysia based on the data from the Department of Statistics Malaysia in the previous year. Heart diseases were the principal causes of death for the population aged 41 and above. Many studies have discovered the factors that cause heart disease and ways to prevent it. Among the ways to prevent heart disease include analysis of the patient’s historical data, developing predictive modeling involving statistical and machine learning techniques, and monitoring health conditions through wearable devices. This paper reviewed the predictive model applied in heart disease prediction using wearable device datasets. Artificial neural networks (ANNs) have grown in popularity in data mining and machine learning for their ability to classify input data into several categories by detecting hidden connections in the data, which is beneficial in predicting correct classifications. Other approaches, such as Naive Bayes, Support Vector Machine, and Decision Tree algorithms, are used to analyze medical data sets to forecast cardiac disease. According to the survey, ANNs are likely to be good for heart disease prediction in terms of classification accuracy on training and test datasets. Penerbit UniMAP 2024-06-04 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/41455/1/100-112%2BA%2BReview%2Bon%2BPredictive%2BModel%2Bfor%2BHeart%2BDisease%2Busing%2BWearable%2BDevices%2BDatasets.pdf Mohd Syafiq Asyraf, Suhaimi and Nor Azuana, Ramli and Noryanti, Muhammad (2024) A Review on Predictive Model for Heart Disease using Wearable Devices Datasets. Applied Mathematics and Computational Intelligence, 13 (2). pp. 100-112. ISSN 2289-1315. (Published) https://ejournal.unimap.edu.my/index.php/amci/article/view/367
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 QA Mathematics
QA75 Electronic computers. Computer science
spellingShingle QA Mathematics
QA75 Electronic computers. Computer science
Mohd Syafiq Asyraf, Suhaimi
Nor Azuana, Ramli
Noryanti, Muhammad
A Review on Predictive Model for Heart Disease using Wearable Devices Datasets
description Heart diseases were the number one killer in Malaysia based on the data from the Department of Statistics Malaysia in the previous year. Heart diseases were the principal causes of death for the population aged 41 and above. Many studies have discovered the factors that cause heart disease and ways to prevent it. Among the ways to prevent heart disease include analysis of the patient’s historical data, developing predictive modeling involving statistical and machine learning techniques, and monitoring health conditions through wearable devices. This paper reviewed the predictive model applied in heart disease prediction using wearable device datasets. Artificial neural networks (ANNs) have grown in popularity in data mining and machine learning for their ability to classify input data into several categories by detecting hidden connections in the data, which is beneficial in predicting correct classifications. Other approaches, such as Naive Bayes, Support Vector Machine, and Decision Tree algorithms, are used to analyze medical data sets to forecast cardiac disease. According to the survey, ANNs are likely to be good for heart disease prediction in terms of classification accuracy on training and test datasets.
format Article
author Mohd Syafiq Asyraf, Suhaimi
Nor Azuana, Ramli
Noryanti, Muhammad
author_facet Mohd Syafiq Asyraf, Suhaimi
Nor Azuana, Ramli
Noryanti, Muhammad
author_sort Mohd Syafiq Asyraf, Suhaimi
title A Review on Predictive Model for Heart Disease using Wearable Devices Datasets
title_short A Review on Predictive Model for Heart Disease using Wearable Devices Datasets
title_full A Review on Predictive Model for Heart Disease using Wearable Devices Datasets
title_fullStr A Review on Predictive Model for Heart Disease using Wearable Devices Datasets
title_full_unstemmed A Review on Predictive Model for Heart Disease using Wearable Devices Datasets
title_sort review on predictive model for heart disease using wearable devices datasets
publisher Penerbit UniMAP
publishDate 2024
url http://umpir.ump.edu.my/id/eprint/41455/1/100-112%2BA%2BReview%2Bon%2BPredictive%2BModel%2Bfor%2BHeart%2BDisease%2Busing%2BWearable%2BDevices%2BDatasets.pdf
http://umpir.ump.edu.my/id/eprint/41455/
https://ejournal.unimap.edu.my/index.php/amci/article/view/367
_version_ 1822924377489932288
score 13.235362

Similar Items