Analytical Results of Heart Attack Prediction Using Data Mining Techniques

Title

Analytical Results of Heart Attack Prediction Using Data Mining Techniques

Creator

Abhijith S.; Mishra S.; Prathap B.

Description

In the modern era of living a fast lifestyle, people are not more conscious of their food eating and lifestyle. Due to these reasons, the chances of having a cardiac-related disease have risen drastically. This paper has studied the various supervised and unsupervised machine learning algorithms in comparative methods with best accuracy. Models like classification algorithms, regression algorithms, and clustering algorithms have been used for this paper. This research paper majorly focuses on patients with certain medical attributes that indicate a higher risk of heart disease. The model almost gives a good accuracy for all the regression and classification models when compared to the clustering models. Among all the algorithms, random forest and decision tree gives better accuracy 2023 IEEE.

Source

Proceedings of IEEE InC4 2023 - 2023 IEEE International Conference on Contemporary Computing and Communications

Date

2023-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Classification; Heart Attack Prediction; K-Means; Knn; Linear Regression; Logistic Regression; Machine Learning; Random Forest Classifier; Regression; Supervised Algorithms; Unsupervised Algorithms

Coverage

Abhijith S., CHRIST(Deemed to Be University), Department of Computer Science and Engineering, Bangalore, India; Mishra S., CHRIST(Deemed to Be University), Department of Computer Science and Engineering, Bangalore, India; Prathap B., CHRIST(Deemed to Be University), Department of Computer Science and Engineering, Bangalore, India

Rights

Restricted Access

Relation

ISBN: 979-835033577-4

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/InC457730.2023.10263275

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174692382&doi=10.1109%2fInC457730.2023.10263275&partnerID=40&md5=502fd9e64a33bb094e639bb30cb88ebe