Algorithm for generating hyperparameter.
<div><p>In the current era, a lot of research is being done in the domain of disease diagnosis using machine learning. In recent times, one of the deadliest respiratory diseases, COVID-19, which causes serious damage to the lungs has claimed a lot of lives globally. Machine learning-base...
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| مؤلفون آخرون: | , , , , |
| منشور في: |
2024
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| _version_ | 1852024782271283200 |
|---|---|
| author | Balraj Preet Kaur (20370832) |
| author2 | Harpreet Singh (677440) Rahul Hans (20370835) Sanjeev Kumar Sharma (5463875) Chetna Sharma (2240488) Md. Mehedi Hassan (11460750) |
| author2_role | author author author author author |
| author_facet | Balraj Preet Kaur (20370832) Harpreet Singh (677440) Rahul Hans (20370835) Sanjeev Kumar Sharma (5463875) Chetna Sharma (2240488) Md. Mehedi Hassan (11460750) |
| author_role | author |
| dc.creator.none.fl_str_mv | Balraj Preet Kaur (20370832) Harpreet Singh (677440) Rahul Hans (20370835) Sanjeev Kumar Sharma (5463875) Chetna Sharma (2240488) Md. Mehedi Hassan (11460750) |
| dc.date.none.fl_str_mv | 2024-12-02T19:01:40Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0308015.t002 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Algorithm_for_generating_hyperparameter_/27946231 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Cancer Science Policy Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified shapely adaptive explanations results obtained show deadliest respiratory diseases causes serious damage superior prediction accuracy adaboost algorithm outperformed respiratory disease prediction machine learning algorithms incorporating hyperparameter optimization machine learning optimized algorithms including accuracy genetic algorithm hyperparameter optimization based optimization xlink "> successful deployment stacking classifier recent times predictions made making use lives globally improved model important issues feature set feature selection feature importance explainable ai ensemble model early diagnosis deadly effects current era based systems assist clinicians |
| dc.title.none.fl_str_mv | Algorithm for generating hyperparameter. |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <div><p>In the current era, a lot of research is being done in the domain of disease diagnosis using machine learning. In recent times, one of the deadliest respiratory diseases, COVID-19, which causes serious damage to the lungs has claimed a lot of lives globally. Machine learning-based systems can assist clinicians in the early diagnosis of the disease, which can reduce the deadly effects of the disease. For the successful deployment of these machine learning-based systems, hyperparameter-based optimization and feature selection are important issues. Motivated by the above, in this proposal, we design an improved model to predict the existence of respiratory disease among patients by incorporating hyperparameter optimization and feature selection. To optimize the parameters of the machine learning algorithms, hyperparameter optimization with a genetic algorithm is proposed and to reduce the size of the feature set, feature selection is performed using binary grey wolf optimization algorithm. Moreover, to enhance the efficacy of the predictions made by hyperparameter-optimized machine learning models, an ensemble model is proposed using a stacking classifier. Also, explainable AI was incorporated to define the feature importance by making use of Shapely adaptive explanations (SHAP) values. For the experimentation, the publicly accessible Mexico clinical dataset of COVID-19 was used. The results obtained show that the proposed model has superior prediction accuracy in comparison to its counterparts. Moreover, among all the hyperparameter-optimized algorithms, adaboost algorithm outperformed all the other hyperparameter-optimized algorithms. The various performance assessment metrics, including accuracy, precision, recall, AUC, and F1-score, were used to assess the results.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_acbc2e66a59b0d2d05f7ffbaa5ecc3cd |
| identifier_str_mv | 10.1371/journal.pone.0308015.t002 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/27946231 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Algorithm for generating hyperparameter.Balraj Preet Kaur (20370832)Harpreet Singh (677440)Rahul Hans (20370835)Sanjeev Kumar Sharma (5463875)Chetna Sharma (2240488)Md. Mehedi Hassan (11460750)CancerScience PolicyBiological Sciences not elsewhere classifiedMathematical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedshapely adaptive explanationsresults obtained showdeadliest respiratory diseasescauses serious damagesuperior prediction accuracyadaboost algorithm outperformedrespiratory disease predictionmachine learning algorithmsincorporating hyperparameter optimizationmachine learningoptimized algorithmsincluding accuracygenetic algorithmhyperparameter optimizationbased optimizationxlink ">successful deploymentstacking classifierrecent timespredictions mademaking uselives globallyimproved modelimportant issuesfeature setfeature selectionfeature importanceexplainable aiensemble modelearly diagnosisdeadly effectscurrent erabased systemsassist clinicians<div><p>In the current era, a lot of research is being done in the domain of disease diagnosis using machine learning. In recent times, one of the deadliest respiratory diseases, COVID-19, which causes serious damage to the lungs has claimed a lot of lives globally. Machine learning-based systems can assist clinicians in the early diagnosis of the disease, which can reduce the deadly effects of the disease. For the successful deployment of these machine learning-based systems, hyperparameter-based optimization and feature selection are important issues. Motivated by the above, in this proposal, we design an improved model to predict the existence of respiratory disease among patients by incorporating hyperparameter optimization and feature selection. To optimize the parameters of the machine learning algorithms, hyperparameter optimization with a genetic algorithm is proposed and to reduce the size of the feature set, feature selection is performed using binary grey wolf optimization algorithm. Moreover, to enhance the efficacy of the predictions made by hyperparameter-optimized machine learning models, an ensemble model is proposed using a stacking classifier. Also, explainable AI was incorporated to define the feature importance by making use of Shapely adaptive explanations (SHAP) values. For the experimentation, the publicly accessible Mexico clinical dataset of COVID-19 was used. The results obtained show that the proposed model has superior prediction accuracy in comparison to its counterparts. Moreover, among all the hyperparameter-optimized algorithms, adaboost algorithm outperformed all the other hyperparameter-optimized algorithms. The various performance assessment metrics, including accuracy, precision, recall, AUC, and F1-score, were used to assess the results.</p></div>2024-12-02T19:01:40ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1371/journal.pone.0308015.t002https://figshare.com/articles/dataset/Algorithm_for_generating_hyperparameter_/27946231CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/279462312024-12-02T19:01:40Z |
| spellingShingle | Algorithm for generating hyperparameter. Balraj Preet Kaur (20370832) Cancer Science Policy Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified shapely adaptive explanations results obtained show deadliest respiratory diseases causes serious damage superior prediction accuracy adaboost algorithm outperformed respiratory disease prediction machine learning algorithms incorporating hyperparameter optimization machine learning optimized algorithms including accuracy genetic algorithm hyperparameter optimization based optimization xlink "> successful deployment stacking classifier recent times predictions made making use lives globally improved model important issues feature set feature selection feature importance explainable ai ensemble model early diagnosis deadly effects current era based systems assist clinicians |
| status_str | publishedVersion |
| title | Algorithm for generating hyperparameter. |
| title_full | Algorithm for generating hyperparameter. |
| title_fullStr | Algorithm for generating hyperparameter. |
| title_full_unstemmed | Algorithm for generating hyperparameter. |
| title_short | Algorithm for generating hyperparameter. |
| title_sort | Algorithm for generating hyperparameter. |
| topic | Cancer Science Policy Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified shapely adaptive explanations results obtained show deadliest respiratory diseases causes serious damage superior prediction accuracy adaboost algorithm outperformed respiratory disease prediction machine learning algorithms incorporating hyperparameter optimization machine learning optimized algorithms including accuracy genetic algorithm hyperparameter optimization based optimization xlink "> successful deployment stacking classifier recent times predictions made making use lives globally improved model important issues feature set feature selection feature importance explainable ai ensemble model early diagnosis deadly effects current era based systems assist clinicians |