ML-Based Prediction Model for Cardiovascular Disease

Title

ML-Based Prediction Model for Cardiovascular Disease

Creator

Nagavelli U.; Samanta D.; Thomas B.

Description

In this paper, the prediction of cardiovascular disease model based on the machine learning algorithm is implemented. In medical system applications, data mining and machine learning play an important role. Machine learning algorithms will predict heart disease or cardiovascular disease. Initially, online datasets are applied to preprocessing stage. Preprocessing stage will divide the data from baseline data. In the same way, CVD events are collected from data follow-ups. After that, data will be screened using the regression model. The regression model consists of logistic regression, support vector machine, nae Bayes, random forest, and K-nearest neighbors. Based on the techniques, the disease will be classified. Before classification, a testing procedure will be performed. At last from results, it can observe that accuracy, misclassification, and reliability will be increased in a very effective way. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Source

Lecture Notes in Networks and Systems, Vol-490, pp. 91-98.

Date

2023-01-01

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Cardiovascular disease (CVD); Logistic regression; Nae Bayes; Random forest

Coverage

Nagavelli U., Dayananda Sagar Research Foundation, University of Mysore (UoM), Karnataka, Mysore, India; Samanta D., Dayananda Sagar Research Foundation, University of Mysore (UoM), Karnataka, Mysore, India, Department of Computer Science, CHRIST Deemed to be University, Karnataka, Bangalore, India; Thomas B., Department of Computer Science, CHRIST Deemed to be University, Karnataka, Bangalore, India

Rights

Restricted Access

Relation

ISSN: 23673370; ISBN: 978-981194051-4

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-981-19-4052-1_11

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85138760730&doi=10.1007%2f978-981-19-4052-1_11&partnerID=40&md5=d149662a92dcb6a3c3e504032593a768