Cardiovascular Disease (CVD) Prediction Using Machine Learning Techniques With XGBoost Feature Importance Analysis

Nurzahan Akter Joly; Zahrul Jannat Peya; Romjan Munchi

doi:10.36948/ijfmr.2023.v05i05.7715

Cardiovascular Disease (CVD) Prediction Using Machine Learning Techniques With XGBoost Feature Importance Analysis

Author(s)	Nurzahan Akter Joly, Zahrul Jannat Peya, Romjan Munchi
Country	Bangladesh
Abstract	Cardiovascular diseases (CVD) are a type of illnesses in the cardiovascular system including coronary, rheumatic heart and cerebrovascular. The leading causes of disease burden and mortality worldwide are CVDs. CVD can cause a wide range of consequences, which can lower standard of life and sometimes cause death. This emphasizes the requirement for the establishment of a technique that can ensure an exact and prompt prediction of the risk of CVD in patients. This study investigates effective CVD prediction system using several Machine Learning (ML) classification models. Rigorous data analysis through several preprocessing techniques as well as feature importance analysis has been performed through Spearman Correlation Analysis and XGboost feature importance technique. Finally, classification has been accomplished through Random Forest (RF), Support Vector Machine (SVM) and Logistic Regression (LR) using a standard benchmark dataset collected from IEEEDataPort. Highest accuracy of 95% has been achieved through Random Forest (RF). The findings of this study will assist professionals in the medical field in the early diagnosis of cardiovascular disease in patients.
Keywords	Cardiovascular Disease (CVD), Spearman Correlation Analysis, XGBoost Feature Importance, Random Forest, Support Vector Machine, Logistic Regression
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 5, Issue 5, September-October 2023
Published On	2023-10-24
Cite This	Cardiovascular Disease (CVD) Prediction Using Machine Learning Techniques With XGBoost Feature Importance Analysis - Nurzahan Akter Joly, Zahrul Jannat Peya, Romjan Munchi - IJFMR Volume 5, Issue 5, September-October 2023. DOI 10.36948/ijfmr.2023.v05i05.7715
DOI	https://doi.org/10.36948/ijfmr.2023.v05i05.7715
Short DOI	https://doi.org/gszvsb

View / Download PDF File

E-ISSN 2582-2160

doi

CrossRef DOI is assigned to each research paper published in our journal.

IJFMR DOI prefix is
10.36948/ijfmr

Downloads

Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 6 Isu 3 Cover Page Vol 6 Isu 2 Cover Page Vol 6 Isu 1

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA

About IJFMR Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+91-9898-5948-55	editor@ijfmr.com

International Journal For Multidisciplinary Research

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Cardiovascular Disease (CVD) Prediction Using Machine Learning Techniques With XGBoost Feature Importance Analysis

Share this