ML-Based Prediction Model for Cardiovascular Disease
subject: Cardiovascular disease (CVD); Logistic regression; Nae Bayes; Random forest
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.
creator: Nagavelli U.; Samanta D.; Thomas B.
source: Lecture Notes in Networks and Systems, Vol-490, pp. 91-98.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2023-01-01
rights: Restricted Access
relation: ISSN: 23673370; ISBN: 978-981194051-4
format: Online
language: English
type: Conference paper
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