Stacking ensemble based hyperparameters to diagnosing of heart disease: Future works
Background: Heart disease is one of the most recurrent and worrying health problems today, due to its multiple complications, including: stroke, cardiac arrest, retinopathy, etc. Objective: Propose a method and 4 Stacking models based on hyperparameters to diagnose heart disease. In addition, a web...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-03-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123024001476 |
| _version_ | 1827310601435611136 |
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| author | Alfredo Daza Juana Bobadilla Juan Carlos Herrera Angelica Medina Nemias Saboya Karoline Zavaleta Segundo Siguenas |
| author_facet | Alfredo Daza Juana Bobadilla Juan Carlos Herrera Angelica Medina Nemias Saboya Karoline Zavaleta Segundo Siguenas |
| author_sort | Alfredo Daza |
| collection | DOAJ |
| description | Background: Heart disease is one of the most recurrent and worrying health problems today, due to its multiple complications, including: stroke, cardiac arrest, retinopathy, etc. Objective: Propose a method and 4 Stacking models based on hyperparameters to diagnose heart disease. In addition, a web interface was developed with the best model proposed in this study. Methods: First, the dataset used was from the Heart Disease Cleveland ICU, which was 918 patient records and 12 attributes. Therefore, the paper was composed of the following stages: Cleaning and Pre-processing; Describe data; Training and testing data; Cross validation; Calibration of models; and modelling and evaluation, also compare the different techniques proposed to predict heart disease using Stacking ensemble based on hyperparameters taking into account the performance evaluation parameters. Results: Stacking 1 (Logistic regression) with oversampling and AdaBoost-SVM with hyperparameter in the test obtained higher Accuracy (88.24%), and ROC Curve (92.00%), while too Stacking 1 (Logistic regression) with oversampling reached a better Precision (88.54%), but the AdaBoost-SVM algorithm using hyperparameter achieved a high value of Sensitivity (88.14%) and F1-Score (88.19%). Conclusions: Implementing 4 stacking models based on hyperparameters, it helps to make an early diagnosis of heart disease and greater precision, and decrease the quantity of deceases caused by it. Therefore, by using the combined method, an improvement in heart disease prediction was observed, surpassing the performance of the independent algorithms used. |
| first_indexed | 2024-03-08T00:00:03Z |
| format | Article |
| id | doaj.art-b711671435ce4b48b1143f92e31524e8 |
| institution | Directory Open Access Journal |
| issn | 2590-1230 |
| language | English |
| last_indexed | 2024-04-24T20:02:49Z |
| publishDate | 2024-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj.art-b711671435ce4b48b1143f92e31524e82024-03-24T07:01:05ZengElsevierResults in Engineering2590-12302024-03-0121101894Stacking ensemble based hyperparameters to diagnosing of heart disease: Future worksAlfredo Daza0Juana Bobadilla1Juan Carlos Herrera2Angelica Medina3Nemias Saboya4Karoline Zavaleta5Segundo Siguenas6Faculty of Engineering and Architecture, School of Systems Engineering, Universidad César Vallejo, Lima, Peru; Corresponding author.Faculty of Pedagogy and Physical Culture, Alternative Basic Education School - Primary, Universidad Nacional de Educación Enrique Guzmán y Valle, Lima, PeruFaculty of Systems Engineering, Professional Academic Career of Systems Engineering, Universidad Andina Néstor Cáceres Velásquez, Puno, PeruFaculty of Pedagogy and Physical Culture, Department of Educational Sciences, Universidad Nacional de Educación Enrique Guzmán y Valle, Lima, PeruFaculty of Engineering and Architecture, School of Systems Engineering, Universidad César Vallejo, Lima, PeruFaculty of Business Sciences, School of Management, Universidad César Vallejo, Trujillo, PeruStrategy Data Consulting - SDC Consulting, Lima, PeruBackground: Heart disease is one of the most recurrent and worrying health problems today, due to its multiple complications, including: stroke, cardiac arrest, retinopathy, etc. Objective: Propose a method and 4 Stacking models based on hyperparameters to diagnose heart disease. In addition, a web interface was developed with the best model proposed in this study. Methods: First, the dataset used was from the Heart Disease Cleveland ICU, which was 918 patient records and 12 attributes. Therefore, the paper was composed of the following stages: Cleaning and Pre-processing; Describe data; Training and testing data; Cross validation; Calibration of models; and modelling and evaluation, also compare the different techniques proposed to predict heart disease using Stacking ensemble based on hyperparameters taking into account the performance evaluation parameters. Results: Stacking 1 (Logistic regression) with oversampling and AdaBoost-SVM with hyperparameter in the test obtained higher Accuracy (88.24%), and ROC Curve (92.00%), while too Stacking 1 (Logistic regression) with oversampling reached a better Precision (88.54%), but the AdaBoost-SVM algorithm using hyperparameter achieved a high value of Sensitivity (88.14%) and F1-Score (88.19%). Conclusions: Implementing 4 stacking models based on hyperparameters, it helps to make an early diagnosis of heart disease and greater precision, and decrease the quantity of deceases caused by it. Therefore, by using the combined method, an improvement in heart disease prediction was observed, surpassing the performance of the independent algorithms used.http://www.sciencedirect.com/science/article/pii/S2590123024001476Machine learningPredictionHeart diseaseHyperparametersStacking |
| spellingShingle | Alfredo Daza Juana Bobadilla Juan Carlos Herrera Angelica Medina Nemias Saboya Karoline Zavaleta Segundo Siguenas Stacking ensemble based hyperparameters to diagnosing of heart disease: Future works Results in Engineering Machine learning Prediction Heart disease Hyperparameters Stacking |
| title | Stacking ensemble based hyperparameters to diagnosing of heart disease: Future works |
| title_full | Stacking ensemble based hyperparameters to diagnosing of heart disease: Future works |
| title_fullStr | Stacking ensemble based hyperparameters to diagnosing of heart disease: Future works |
| title_full_unstemmed | Stacking ensemble based hyperparameters to diagnosing of heart disease: Future works |
| title_short | Stacking ensemble based hyperparameters to diagnosing of heart disease: Future works |
| title_sort | stacking ensemble based hyperparameters to diagnosing of heart disease future works |
| topic | Machine learning Prediction Heart disease Hyperparameters Stacking |
| url | http://www.sciencedirect.com/science/article/pii/S2590123024001476 |
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