Heart Failure Prediction Using XGB Classifier, Logistic Regression and Support Vector Classifier

Vinod Jain; Mayank Agrawal

doi:10.1109/InCACCT57535.2023.10141752

Heart Failure Prediction Using XGB Classifier, Logistic Regression and Support Vector Classifier

Vinod Jain, Mayank Agrawal

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Scopus citations

Abstract

Heart updated failure is a very serious medical issue nowadays. It causes a lot of deaths all over the world. The bad lifestyle, bad eating habits, unusual food timings are some of the factors responsible for this disease. Artificial intelligence and machine learning is a technology which is used by many researchers for prediction of diseases. Machine Learning (ML) algorithms provide some models which are first trained on a training data and then can be used to test the input data. These models are very helpful in prediction of heart disease. In this work XGBoost, Logistic Regression and Support Vector Machine ML models are used to predict heart disease. Cross validation method is used in this work which improved the prediction accuracy of all the three models. Outcoming results ensure that the XGBoost classifier is the best ML model for heart disease prediction as compared to Logistic Regression and Support vector Machine.

Original language	English
Title of host publication	2023 International Conference on Advancement in Computation and Computer Technologies, InCACCT 2023
Editors	Rakesh Kumar, Rakesh Kumar, Meenu Gupta, Meenu Gupta, Ritesh Srivastava, Ritesh Srivastava
Publisher	Institute of Electrical and Electronics Engineers
Pages	338-342
Number of pages	5
ISBN (Electronic)	9798350396485
DOIs	https://doi.org/10.1109/InCACCT57535.2023.10141752
State	Published - 1 Jan 2023
Externally published	Yes
Event	2023 International Conference on Advancement in Computation and Computer Technologies, InCACCT 2023 - Gharuan, India Duration: 5 May 2023 → 6 May 2023

Publication series

Name	2023 International Conference on Advancement in Computation and Computer Technologies, InCACCT 2023

Conference

Conference	2023 International Conference on Advancement in Computation and Computer Technologies, InCACCT 2023
Country/Territory	India
City	Gharuan
Period	5/05/23 → 6/05/23

Keywords

Artificial Intelligence
Breast Cancer Prediction
Logistic Regression
Machine Learning
Support Vector Machine
XGB Classifier

ASJC Scopus subject areas

Computer Science Applications
Computer Vision and Pattern Recognition
Information Systems and Management
Statistics, Probability and Uncertainty
Computational Mathematics
Health Informatics
Artificial Intelligence

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/InCACCT57535.2023.10141752

Cite this

Jain, V., & Agrawal, M. (2023). Heart Failure Prediction Using XGB Classifier, Logistic Regression and Support Vector Classifier. In R. Kumar, R. Kumar, M. Gupta, M. Gupta, R. Srivastava, & R. Srivastava (Eds.), 2023 International Conference on Advancement in Computation and Computer Technologies, InCACCT 2023 (pp. 338-342). (2023 International Conference on Advancement in Computation and Computer Technologies, InCACCT 2023). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/InCACCT57535.2023.10141752

Jain, Vinod ; Agrawal, Mayank. / Heart Failure Prediction Using XGB Classifier, Logistic Regression and Support Vector Classifier. 2023 International Conference on Advancement in Computation and Computer Technologies, InCACCT 2023. editor / Rakesh Kumar ; Rakesh Kumar ; Meenu Gupta ; Meenu Gupta ; Ritesh Srivastava ; Ritesh Srivastava. Institute of Electrical and Electronics Engineers, 2023. pp. 338-342 (2023 International Conference on Advancement in Computation and Computer Technologies, InCACCT 2023).

@inproceedings{a4f36dfda7f545839708f89352afdbb0,

title = "Heart Failure Prediction Using XGB Classifier, Logistic Regression and Support Vector Classifier",

abstract = "Heart updated failure is a very serious medical issue nowadays. It causes a lot of deaths all over the world. The bad lifestyle, bad eating habits, unusual food timings are some of the factors responsible for this disease. Artificial intelligence and machine learning is a technology which is used by many researchers for prediction of diseases. Machine Learning (ML) algorithms provide some models which are first trained on a training data and then can be used to test the input data. These models are very helpful in prediction of heart disease. In this work XGBoost, Logistic Regression and Support Vector Machine ML models are used to predict heart disease. Cross validation method is used in this work which improved the prediction accuracy of all the three models. Outcoming results ensure that the XGBoost classifier is the best ML model for heart disease prediction as compared to Logistic Regression and Support vector Machine.",

keywords = "Artificial Intelligence, Breast Cancer Prediction, Logistic Regression, Machine Learning, Support Vector Machine, XGB Classifier",

author = "Vinod Jain and Mayank Agrawal",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 International Conference on Advancement in Computation and Computer Technologies, InCACCT 2023 ; Conference date: 05-05-2023 Through 06-05-2023",

year = "2023",

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Jain, V & Agrawal, M 2023, Heart Failure Prediction Using XGB Classifier, Logistic Regression and Support Vector Classifier. in R Kumar, R Kumar, M Gupta, M Gupta, R Srivastava & R Srivastava (eds), 2023 International Conference on Advancement in Computation and Computer Technologies, InCACCT 2023. 2023 International Conference on Advancement in Computation and Computer Technologies, InCACCT 2023, Institute of Electrical and Electronics Engineers, pp. 338-342, 2023 International Conference on Advancement in Computation and Computer Technologies, InCACCT 2023, Gharuan, India, 5/05/23. https://doi.org/10.1109/InCACCT57535.2023.10141752

Heart Failure Prediction Using XGB Classifier, Logistic Regression and Support Vector Classifier. / Jain, Vinod; Agrawal, Mayank.
2023 International Conference on Advancement in Computation and Computer Technologies, InCACCT 2023. ed. / Rakesh Kumar; Rakesh Kumar; Meenu Gupta; Meenu Gupta; Ritesh Srivastava; Ritesh Srivastava. Institute of Electrical and Electronics Engineers, 2023. p. 338-342 (2023 International Conference on Advancement in Computation and Computer Technologies, InCACCT 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Heart Failure Prediction Using XGB Classifier, Logistic Regression and Support Vector Classifier

AU - Jain, Vinod

AU - Agrawal, Mayank

PY - 2023/1/1

Y1 - 2023/1/1

N2 - Heart updated failure is a very serious medical issue nowadays. It causes a lot of deaths all over the world. The bad lifestyle, bad eating habits, unusual food timings are some of the factors responsible for this disease. Artificial intelligence and machine learning is a technology which is used by many researchers for prediction of diseases. Machine Learning (ML) algorithms provide some models which are first trained on a training data and then can be used to test the input data. These models are very helpful in prediction of heart disease. In this work XGBoost, Logistic Regression and Support Vector Machine ML models are used to predict heart disease. Cross validation method is used in this work which improved the prediction accuracy of all the three models. Outcoming results ensure that the XGBoost classifier is the best ML model for heart disease prediction as compared to Logistic Regression and Support vector Machine.

AB - Heart updated failure is a very serious medical issue nowadays. It causes a lot of deaths all over the world. The bad lifestyle, bad eating habits, unusual food timings are some of the factors responsible for this disease. Artificial intelligence and machine learning is a technology which is used by many researchers for prediction of diseases. Machine Learning (ML) algorithms provide some models which are first trained on a training data and then can be used to test the input data. These models are very helpful in prediction of heart disease. In this work XGBoost, Logistic Regression and Support Vector Machine ML models are used to predict heart disease. Cross validation method is used in this work which improved the prediction accuracy of all the three models. Outcoming results ensure that the XGBoost classifier is the best ML model for heart disease prediction as compared to Logistic Regression and Support vector Machine.

KW - Artificial Intelligence

KW - Breast Cancer Prediction

KW - Logistic Regression

KW - Machine Learning

KW - Support Vector Machine

KW - XGB Classifier

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M3 - Conference contribution

AN - SCOPUS:85163387298

T3 - 2023 International Conference on Advancement in Computation and Computer Technologies, InCACCT 2023

SP - 338

EP - 342

BT - 2023 International Conference on Advancement in Computation and Computer Technologies, InCACCT 2023

A2 - Kumar, Rakesh

A2 - Gupta, Meenu

A2 - Srivastava, Ritesh

PB - Institute of Electrical and Electronics Engineers

T2 - 2023 International Conference on Advancement in Computation and Computer Technologies, InCACCT 2023

Y2 - 5 May 2023 through 6 May 2023

ER -

Jain V, Agrawal M. Heart Failure Prediction Using XGB Classifier, Logistic Regression and Support Vector Classifier. In Kumar R, Kumar R, Gupta M, Gupta M, Srivastava R, Srivastava R, editors, 2023 International Conference on Advancement in Computation and Computer Technologies, InCACCT 2023. Institute of Electrical and Electronics Engineers. 2023. p. 338-342. (2023 International Conference on Advancement in Computation and Computer Technologies, InCACCT 2023). doi: 10.1109/InCACCT57535.2023.10141752

Heart Failure Prediction Using XGB Classifier, Logistic Regression and Support Vector Classifier

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

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