Classification of Heart Failure Using Machine Learning: A Comparative Study

被引：1

作者：

Chulde-Fernandez, Bryan ^{[1
]}

Enriquez-Ortega, Denisse ^{[1
]}

Guevara, Cesar ^{[2
]}

Navas, Paulo ^{[1
]}

Tirado-Espin, Andres ^{[3
]}

Vizcaino-Imacana, Paulina ^{[4
]}

Villalba-Meneses, Fernando ^{[1
]}

Cadena-Morejon, Carolina ^{[3
]}

Almeida-Galarraga, Diego ^{[1
]}

Acosta-Vargas, Patricia ^{[5
]}

机构：

[1] Yachay Tech Univ, Sch Biol Sci & Engn, Hacienda San Jose S-N, San Miguel De Urcuqui 100119, Ecuador

[2] Cunef Univ, Quantitat Methods Dept, Madrid 28040, Spain

[3] Univ Yachay Tech, Sch Math & Computat Sci, San Miguel De Urcuqui 100119, Ecuador

[4] UIDE Int Univ Ecuador, Fac Tech Sci, Sch Comp Sci, Quito 170501, Ecuador

[5] Univ Las Amer, Intelligent & Interact Syst Lab, Quito 170125, Ecuador

来源：

LIFE-BASEL | 2025年 / 15卷 / 03期

关键词：

heart failure; machine learning; classification; feature extraction; diagnosis; CHALLENGES; MANAGEMENT; DISEASE;

D O I：

10.3390/life15030496

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

Several machine learning classification algorithms were evaluated using a dataset focused on heart failure. Results obtained from logistic regression, random forest, decision tree, K-nearest neighbors, and multilayer perceptron (MLP) were compared to obtain the best model. The random forest method obtained specificity = 0.93, AUC = 0.97, and Matthews correlation coefficient (MCC) = 0.83. The accuracy was high; therefore, it was considered the best model. On the other hand, K-nearest neighbors and MLP (multi-layer perceptron) showed lower accuracy rates. These results confirm the effectiveness of the random forest method in identifying heart failure cases. This study underlines that the number of features, feature selection and quality, model type, and hyperparameter fit are also critical in these studies, as well as the importance of using machine learning techniques.

引用

页数：18

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