Classification of Heart Failure Using Machine Learning: A Comparative Study
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 o...
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MDPI AG
2025-03-01
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| Online Access: | https://www.mdpi.com/2075-1729/15/3/496 |
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| author | Bryan Chulde-Fernández Denisse Enríquez-Ortega Cesar Guevara Paulo Navas Andrés Tirado-Espín Paulina Vizcaíno-Imacaña Fernando Villalba-Meneses Carolina Cadena-Morejon Diego Almeida-Galarraga Patricia Acosta-Vargas |
| author_facet | Bryan Chulde-Fernández Denisse Enríquez-Ortega Cesar Guevara Paulo Navas Andrés Tirado-Espín Paulina Vizcaíno-Imacaña Fernando Villalba-Meneses Carolina Cadena-Morejon Diego Almeida-Galarraga Patricia Acosta-Vargas |
| author_sort | Bryan Chulde-Fernández |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-2fb018b43bb3471a8430799863acf9d3 |
| institution | OA Journals |
| issn | 2075-1729 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Life |
| spelling | doaj-art-2fb018b43bb3471a8430799863acf9d32025-08-20T01:49:00ZengMDPI AGLife2075-17292025-03-0115349610.3390/life15030496Classification of Heart Failure Using Machine Learning: A Comparative StudyBryan Chulde-Fernández0Denisse Enríquez-Ortega1Cesar Guevara2Paulo Navas3Andrés Tirado-Espín4Paulina Vizcaíno-Imacaña5Fernando Villalba-Meneses6Carolina Cadena-Morejon7Diego Almeida-Galarraga8Patricia Acosta-Vargas9School of Biological Sciences and Engineering, Yachay Tech University, Hacienda San José s/n, San Miguel de Urcuquí 100119, EcuadorSchool of Biological Sciences and Engineering, Yachay Tech University, Hacienda San José s/n, San Miguel de Urcuquí 100119, EcuadorQuantitative Methods Department, CUNEF University, 28040 Madrid, SpainSchool of Biological Sciences and Engineering, Yachay Tech University, Hacienda San José s/n, San Miguel de Urcuquí 100119, EcuadorSchool of Mathematical and Computational Sciences, Universidad Yachay Tech, San Miguel de Urcuquí 100119, EcuadorFaculty of Technical Sciences, School of Computer Science, UIDE-International University of Ecuador, Quito 170501, EcuadorSchool of Biological Sciences and Engineering, Yachay Tech University, Hacienda San José s/n, San Miguel de Urcuquí 100119, EcuadorSchool of Mathematical and Computational Sciences, Universidad Yachay Tech, San Miguel de Urcuquí 100119, EcuadorSchool of Biological Sciences and Engineering, Yachay Tech University, Hacienda San José s/n, San Miguel de Urcuquí 100119, EcuadorIntelligent and Interactive Systems Laboratory, Universidad de Las Américas, Quito 170125, EcuadorSeveral 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.https://www.mdpi.com/2075-1729/15/3/496heart failuremachine learningclassificationfeature extractiondiagnosis |
| spellingShingle | Bryan Chulde-Fernández Denisse Enríquez-Ortega Cesar Guevara Paulo Navas Andrés Tirado-Espín Paulina Vizcaíno-Imacaña Fernando Villalba-Meneses Carolina Cadena-Morejon Diego Almeida-Galarraga Patricia Acosta-Vargas Classification of Heart Failure Using Machine Learning: A Comparative Study Life heart failure machine learning classification feature extraction diagnosis |
| title | Classification of Heart Failure Using Machine Learning: A Comparative Study |
| title_full | Classification of Heart Failure Using Machine Learning: A Comparative Study |
| title_fullStr | Classification of Heart Failure Using Machine Learning: A Comparative Study |
| title_full_unstemmed | Classification of Heart Failure Using Machine Learning: A Comparative Study |
| title_short | Classification of Heart Failure Using Machine Learning: A Comparative Study |
| title_sort | classification of heart failure using machine learning a comparative study |
| topic | heart failure machine learning classification feature extraction diagnosis |
| url | https://www.mdpi.com/2075-1729/15/3/496 |
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