International Journal For Multidisciplinary Research

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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
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