Earticle

초록

영어

This study presents a comprehensive evaluation of various machine learning models for predicting heart failure outcomes. Leveraging a data set of clinical records, the performance of Logistic Regression, Support Vector Machine (SVM), Random Forest, Soft Voting ensemble, and XGBoost models are rigorously assessed using multiple evaluation metrics, including accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC). The analysis reveals that the XGBoost model outperforms the other techniques across all metrics, exhibiting the highest AUC score, indicating superior discriminative ability in distinguishing between patients with and without heart failure. Furthermore, the study highlights the importance of feature importance analysis provided by XGBoost, offering valuable insights into the most influential predictors of heart failure, which can inform clinical decision-making and patient management strategies. The research also underscores the significance of balancing precision and recall, as reflected by the F1-score, in medical applications to minimize the consequences of false negatives.

목차

Abstract
1. Introduction
1.1 Research background
2. Literature review
3. Data
3.1 Analysis of the variable correlations
3.2 Data normalization
4. Theoretical foundations of machine learning models
4.1 Logistic regression
4.2 Random Forest
4.3 Support Vector Machine (SVM)
4.4 XGBoost
4.5 Soft Voting
5. Experiment
5.1 Hyperparameter optimization
5.2 Data split and cross validation
5.3 Experimental result
6. Conclusion
References

키워드

저자

• ByungJoo Kim [ Professor, Department of Electrical and Electronics Engineering Youngsan University, Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Internet, Broadcasting and Communication

• 간기

  계간

• pISSN

  2288-4920

• eISSN

  2288-4939

• 수록기간

  2009~2025

• 십진분류

  KDC 326 DDC 380