Machine Learning Technology-Based Heart Disease Detection Models

Title

Machine Learning Technology-Based Heart Disease Detection Models

Creator

Nagavelli U.; Samanta D.; Chakraborty P.

Description

At present, a multifaceted clinical disease known as heart failure disease can affect a greater number of people in the world. In the early stages, to evaluate and diagnose the disease of heart failure, cardiac centers and hospitals are heavily based on ECG. The ECG can be considered as a regular tool. Heart disease early detection is a critical concern in healthcare services (HCS). This paper presents the different machine learning technologies based on heart disease detection brief analysis. Firstly, Nae Bayes with a weighted approach is used for predicting heart disease. The second one, according to the features of frequency domain, time domain, and information theory, is automatic and analyze ischemic heart disease localization/detection. Two classifiers such as support vector machine (SVM) with XGBoost with the best performance are selected for the classification in this method. The third one is the heart failure automatic identification method by using an improved SVM based on the duality optimization scheme also analyzed. Finally, for a clinical decision support system (CDSS), an effective heart disease prediction model (HDPM) is used, which includes density-based spatial clustering of applications with noise (DBSCAN) for outlier detection and elimination, a hybrid synthetic minority over-sampling technique-edited nearest neighbor (SMOTE-ENN) for balancing the training data distribution, and XGBoost for heart disease prediction. Machine learning can be applied in the medical industry for disease diagnosis, detection, and prediction. The major purpose of this paper is to give clinicians a tool to help them diagnose heart problems early on. As a result, it will be easier to treat patients effectively and avoid serious repercussions. This study uses XGBoost to test alternative decision tree classification algorithms in the hopes of improving the accuracy of heart disease diagnosis. In terms of precision, accuracy, f1-measure, and recall as performance parameters above mentioned, four types of machine learning (ML) models are compared. Copyright 2022 Umarani Nagavelli et al.

Source

Journal of Healthcare Engineering, Vol-2022

Date

2022-01-01

Publisher

Hindawi Limited

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; Chakraborty P., Department of Computer Science and Engineering, Comilla University, Cumilla, 3506, Bangladesh

Rights

All Open Access; Gold Open Access; Green Open Access

Relation

ISSN: 20402295; PubMed ID: 35265303

Format

Online

Language

English

Type

Article

Identifier

https://doi.org/10.1155/2022/7351061

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