HeartInspect: Heart Disease Prediction of an Individual Using Naïve Bayes Algorithm

Heart disease is a serious health issue that contributes significantly to the high death worldwide. Therefore, the creation of a reliable system for heart disease prediction is essential for early intervention and better results. Such technologies can help identify at-risk persons and enable prompt...

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Published in:2023 IEEE 11th Conference on Systems, Process and Control, ICSPC 2023 - Proceedings
Main Author: Sabri N.; Shari A.A.; Samah K.A.F.A.; Noordin M.R.M.; Shari A.S.; Mohd Ishak F.; Mustapha W.M.W.; Affendi M.F.A.N.R.
Format: Conference paper
Language:English
Published: Institute of Electrical and Electronics Engineers Inc. 2023
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186683109&doi=10.1109%2fICSPC59664.2023.10420149&partnerID=40&md5=ba8514b1c1c5afaafdfd5360868db11e
id 2-s2.0-85186683109
spelling 2-s2.0-85186683109
Sabri N.; Shari A.A.; Samah K.A.F.A.; Noordin M.R.M.; Shari A.S.; Mohd Ishak F.; Mustapha W.M.W.; Affendi M.F.A.N.R.
HeartInspect: Heart Disease Prediction of an Individual Using Naïve Bayes Algorithm
2023
2023 IEEE 11th Conference on Systems, Process and Control, ICSPC 2023 - Proceedings


10.1109/ICSPC59664.2023.10420149
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186683109&doi=10.1109%2fICSPC59664.2023.10420149&partnerID=40&md5=ba8514b1c1c5afaafdfd5360868db11e
Heart disease is a serious health issue that contributes significantly to the high death worldwide. Therefore, the creation of a reliable system for heart disease prediction is essential for early intervention and better results. Such technologies can help identify at-risk persons and enable prompt preventive interventions by utilizing cutting-edge algorithms and analysing pertinent data. Nonetheless, predicting heart disease is a difficult endeavour, especially in underdeveloped regions with few diagnostic tools and a shortage of trained medical workers. Moreover, the healthcare sector produces a tremendous quantity of data on cardiac disease, yet these important resources are frequently underutilized when it comes to making well-informed decisions. Additionally, pricey heart diagnostics like electrocardiograms are now out of reach for the typical person due to increased living expenses. This present work suggests a Naive Bayes-based cardiac disease prediction system as a solution to these problems. The system makes use of a dataset that includes information about a person's heart disease status, height, weight, physical health, difficulties walking, age group, physical activity, general health, and sleep duration. For training and testing purposes, the dataset is partitioned 80/20. The dataset is analysed using the Naive Bayes technique to determine the chance of cardiac disease. Despite assuming independence among the characteristics, the system shows promising performance, reaching about 71-73% precision in heart disease prediction. Even though this falls short of higher standards, it is nevertheless a noteworthy accomplishment in light of the difficulties mentioned in the problem statements. In summary, the Naive Bayes algorithm-based heart disease prediction system reported in this work shows promise for predictions that are 71-73% accurate. This method helps address the problems involved with cardiac disease prediction, particularly in environments with limited resources, by making use of the data that is currently available and overcoming resource constraints. © 2023 IEEE.
Institute of Electrical and Electronics Engineers Inc.

English
Conference paper

author Sabri N.; Shari A.A.; Samah K.A.F.A.; Noordin M.R.M.; Shari A.S.; Mohd Ishak F.; Mustapha W.M.W.; Affendi M.F.A.N.R.
spellingShingle Sabri N.; Shari A.A.; Samah K.A.F.A.; Noordin M.R.M.; Shari A.S.; Mohd Ishak F.; Mustapha W.M.W.; Affendi M.F.A.N.R.
HeartInspect: Heart Disease Prediction of an Individual Using Naïve Bayes Algorithm
author_facet Sabri N.; Shari A.A.; Samah K.A.F.A.; Noordin M.R.M.; Shari A.S.; Mohd Ishak F.; Mustapha W.M.W.; Affendi M.F.A.N.R.
author_sort Sabri N.; Shari A.A.; Samah K.A.F.A.; Noordin M.R.M.; Shari A.S.; Mohd Ishak F.; Mustapha W.M.W.; Affendi M.F.A.N.R.
title HeartInspect: Heart Disease Prediction of an Individual Using Naïve Bayes Algorithm
title_short HeartInspect: Heart Disease Prediction of an Individual Using Naïve Bayes Algorithm
title_full HeartInspect: Heart Disease Prediction of an Individual Using Naïve Bayes Algorithm
title_fullStr HeartInspect: Heart Disease Prediction of an Individual Using Naïve Bayes Algorithm
title_full_unstemmed HeartInspect: Heart Disease Prediction of an Individual Using Naïve Bayes Algorithm
title_sort HeartInspect: Heart Disease Prediction of an Individual Using Naïve Bayes Algorithm
publishDate 2023
container_title 2023 IEEE 11th Conference on Systems, Process and Control, ICSPC 2023 - Proceedings
container_volume
container_issue
doi_str_mv 10.1109/ICSPC59664.2023.10420149
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186683109&doi=10.1109%2fICSPC59664.2023.10420149&partnerID=40&md5=ba8514b1c1c5afaafdfd5360868db11e
description Heart disease is a serious health issue that contributes significantly to the high death worldwide. Therefore, the creation of a reliable system for heart disease prediction is essential for early intervention and better results. Such technologies can help identify at-risk persons and enable prompt preventive interventions by utilizing cutting-edge algorithms and analysing pertinent data. Nonetheless, predicting heart disease is a difficult endeavour, especially in underdeveloped regions with few diagnostic tools and a shortage of trained medical workers. Moreover, the healthcare sector produces a tremendous quantity of data on cardiac disease, yet these important resources are frequently underutilized when it comes to making well-informed decisions. Additionally, pricey heart diagnostics like electrocardiograms are now out of reach for the typical person due to increased living expenses. This present work suggests a Naive Bayes-based cardiac disease prediction system as a solution to these problems. The system makes use of a dataset that includes information about a person's heart disease status, height, weight, physical health, difficulties walking, age group, physical activity, general health, and sleep duration. For training and testing purposes, the dataset is partitioned 80/20. The dataset is analysed using the Naive Bayes technique to determine the chance of cardiac disease. Despite assuming independence among the characteristics, the system shows promising performance, reaching about 71-73% precision in heart disease prediction. Even though this falls short of higher standards, it is nevertheless a noteworthy accomplishment in light of the difficulties mentioned in the problem statements. In summary, the Naive Bayes algorithm-based heart disease prediction system reported in this work shows promise for predictions that are 71-73% accurate. This method helps address the problems involved with cardiac disease prediction, particularly in environments with limited resources, by making use of the data that is currently available and overcoming resource constraints. © 2023 IEEE.
publisher Institute of Electrical and Electronics Engineers Inc.
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