Stacking ensemble based hyperparameters to diagnosing of heart disease: Future works
Heart disease is one of the most recurrent and worrying health problems today, due to its multiple complications, including: stroke, cardiac arrest, retinopathy, etc. Propose a method and 4 Stacking models based on hyperparameters to diagnose heart disease. In addition, a web interface was developed...
Guardado en:
| Autores principales: | , , , , , , |
|---|---|
| Formato: | Artículo |
| Lenguaje: | inglés |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | Enlace permanente (DOI o Handle) Enlace permanente (DOI o Handle) |
| Etiquetas: |
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1881508943984852992 |
|---|---|
| author | Alfredo Daza Juana Bobadilla Cornelio Juan Carlos Herrera Angélica Medina Nemías Saboya Karoline Zavaleta Segundo Siguenas |
| author_browse | Alfredo Daza Angélica Medina Juan Carlos Herrera Juana Bobadilla Cornelio Karoline Zavaleta Nemías Saboya Segundo Siguenas |
| author_facet | Alfredo Daza Juana Bobadilla Cornelio Juan Carlos Herrera Angélica Medina Nemías Saboya Karoline Zavaleta Segundo Siguenas |
| author_sort | Alfredo Daza |
| collection | Producción científica |
| description | Heart disease is one of the most recurrent and worrying health problems today, due to its multiple complications, including: stroke, cardiac arrest, retinopathy, etc. 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. 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. 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%). 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. |
| format | Article |
| id | oai:cris.upeu.edu.pe:20.500.15037/1111 |
| institution | UPeU |
| language | English |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| record_format | dspace |
| spelling | oai:cris.upeu.edu.pe:20.500.15037/11112026-10-30T16:15:13ZStacking ensemble based hyperparameters to diagnosing of heart disease: Future worksAlfredo DazaJuana Bobadilla CornelioJuan Carlos HerreraAngélica MedinaNemías SaboyaKaroline ZavaletaSegundo SiguenasHyperparameterStackingComputer scienceEnsemble learningArtificial intelligenceMachine learningChemistryOrganic chemistryHyperparameterStackingComputer scienceEnsemble learningArtificial intelligenceMachine learningChemistryHealth Sciences Health Professions Health Information Management Artificial Intelligence in HealthcarePhysical Sciences Computer Science Artificial Intelligence Machine Learning and Data ClassificationPhysical Sciences Computer Science Artificial Intelligence Imbalanced Data Classification TechniquesHeart disease is one of the most recurrent and worrying health problems today, due to its multiple complications, including: stroke, cardiac arrest, retinopathy, etc. 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. 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. 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%). 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.Articlehttps://doi.org/10.1016/j.rineng.2024.10189410.1016/j.rineng.2024.101894https://hdl.handle.net/20.500.15037/1111W4391756923enResults in Engineering2590-12302024-02-122026-06-13T09:46:36Z2026-06-13T09:46:36Z |
| spellingShingle | Hyperparameter Stacking Computer science Ensemble learning Artificial intelligence Machine learning Chemistry Organic chemistry Hyperparameter Stacking Computer science Ensemble learning Artificial intelligence Machine learning Chemistry Health Sciences Health Professions Health Information Management Artificial Intelligence in Healthcare Physical Sciences Computer Science Artificial Intelligence Machine Learning and Data Classification Physical Sciences Computer Science Artificial Intelligence Imbalanced Data Classification Techniques Alfredo Daza Juana Bobadilla Cornelio Juan Carlos Herrera Angélica Medina Nemías Saboya Karoline Zavaleta Segundo Siguenas Stacking ensemble based hyperparameters to diagnosing of heart disease: Future works |
| 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 | Hyperparameter Stacking Computer science Ensemble learning Artificial intelligence Machine learning Chemistry Organic chemistry Hyperparameter Stacking Computer science Ensemble learning Artificial intelligence Machine learning Chemistry Health Sciences Health Professions Health Information Management Artificial Intelligence in Healthcare Physical Sciences Computer Science Artificial Intelligence Machine Learning and Data Classification Physical Sciences Computer Science Artificial Intelligence Imbalanced Data Classification Techniques |
| topic_facet | Hyperparameter Stacking Computer science Ensemble learning Artificial intelligence Machine learning Chemistry Organic chemistry Hyperparameter Stacking Computer science Ensemble learning Artificial intelligence Machine learning Chemistry Health Sciences Health Professions Health Information Management Artificial Intelligence in Healthcare Physical Sciences Computer Science Artificial Intelligence Machine Learning and Data Classification Physical Sciences Computer Science Artificial Intelligence Imbalanced Data Classification Techniques |
| url | https://doi.org/10.1016/j.rineng.2024.101894 https://hdl.handle.net/20.500.15037/1111 |
| work_keys_str_mv | AT alfredodaza stackingensemblebasedhyperparameterstodiagnosingofheartdiseasefutureworks AT juanabobadillacornelio stackingensemblebasedhyperparameterstodiagnosingofheartdiseasefutureworks AT juancarlosherrera stackingensemblebasedhyperparameterstodiagnosingofheartdiseasefutureworks AT angelicamedina stackingensemblebasedhyperparameterstodiagnosingofheartdiseasefutureworks AT nemiassaboya stackingensemblebasedhyperparameterstodiagnosingofheartdiseasefutureworks AT karolinezavaleta stackingensemblebasedhyperparameterstodiagnosingofheartdiseasefutureworks AT segundosiguenas stackingensemblebasedhyperparameterstodiagnosingofheartdiseasefutureworks |