Application of machine learning algorithms in predicting the heart disease in patients
Tools
Tools
A, Razia Sulthana
ORCID: 0000-0001-5331-1310
, A K, Jaithunbi and P, Supraja
(2023)
Application of machine learning algorithms in predicting the heart disease in patients.
In: 2023 Third International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT).
IEEE, Piscataway, New Jersey.
ISBN 9781665494014
(doi:https://doi.org/10.1109/ICAECT57570.2023.10117653)
|
PDF (AAM)
41814_SULTHANA_Application_of_machine_learning_algorithms_in_predicting_the_heart_disease_in_patients.pdf - Accepted Version Download (554kB) | Preview |
Abstract
Healthcare services save the life of human beings by making timely effective decisions. The use of data mining tools is crucial for decision making, forecasting, and disease prediction. In this study, data mining algorithms are applied to predict heart disease. The dataset contains 14 attributes such as age, gender, blood pressure, blood fat, etc. These parameters are analyzed to predict the probability of patients prone to heart disease in future. Initially, the relationship between the parameters is analyzed. Following which Na¨ıve Bayes, decision trees and Na¨ıve Bayes with k-means clustering are applied over it for classification and prediction. These algorithms were employed to train the dataset and create a binary classification. The proposed system shows a better prediction of heart disease. The performance measures of the system are measured, and the obtained results illustrate the system can forecast the probability of developing the heart diseases.
| Item Type: | Conference Proceedings |
|---|---|
| Title of Proceedings: | 2023 Third International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT) |
| Uncontrolled Keywords: | heart disease; data mining; naive Bayes; decision trees; prediction |
| Subjects: | Q Science > QA Mathematics Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Faculty / School / Research Centre / Research Group: | Faculty of Engineering & Science Faculty of Engineering & Science > School of Computing & Mathematical Sciences (CMS) |
| Related URLs: | |
| Last Modified: | 27 Jun 2023 09:20 |
| URI: | http://gala.gre.ac.uk/id/eprint/41814 |
Actions (login required)
 |
View Item |
Downloads
Downloads per month over past year