Predicting heart ailment in patients with varying number of features using data mining techniques

Title

Predicting heart ailment in patients with varying number of features using data mining techniques

Creator

Stella Mary T.R.; Sebastian S.

Description

Data mining can be defined as a process of extracting unknown, verifiable and possibly helpful data from information. Among the various ailments, heart ailment is one of the primary reason behind death of individuals around the globe, hence in order to curb this, a detailed analysis is done using Data Mining. Many a times we limit ourselves with minimal attributes that are required to predict a patient with heart disease. By doing so we are missing on a lot of important attributes that are main causes for heart diseases. Hence, this research aims at considering almost all the important features affecting heart disease and performs the analysis step by step with minimal to maximum set of attributes using Data Mining techniques to predict heart ailments. The various classification methods used are Nae Bayes classifier, Random Forest and Random Tree which are applied on three datasets with different number of attributes but with a common class label. From the analysis performed, it shows that there is a gradual increase in prediction accuracies with the increase in the attributes irrespective of the classifiers used and Nae Bayes and Random Forest algorithms comparatively outperforms with these sets of data. 2019 Institute of Advanced Engineering and Science.

Source

International Journal of Electrical and Computer Engineering, Vol-9, No. 4, pp. 2675-2681.

Date

2019-01-01

Publisher

Institute of Advanced Engineering and Science

Subject

Data mining; Heart ailments; Nae bayes classifier; Random forest; Random tree

Coverage

Stella Mary T.R., Department of Computer Science, Christ University, India; Sebastian S., Department of Computer Science, Christ University, India

Rights

All Open Access; Gold Open Access; Green Open Access

Relation

ISSN: 20888708

Format

Online

Language

English

Type

Article

Identifier

https://doi.org/10.11591/ijece.v9i4.pp2675-2681

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85066249783&doi=10.11591%2fijece.v9i4.pp2675-2681&partnerID=40&md5=50ef94dbc71ff4ba1bcc9d49586fbce4