Addressing Binary Classification over Class Imbalanced Clinical Datasets Using Computationally Intelligent Techniques
<p dir="ltr">Nowadays, healthcare is the prime need of every human being in the world, and clinical datasets play an important role in developing an intelligent healthcare system for monitoring the health of people. Mostly, the real-world datasets are inherently class imbalanced, cli...
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | , , , , , , , |
| Published: |
2022
|
| Subjects: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1864513531095810048 |
|---|---|
| author | Vinod Kumar (48743) |
| author2 | Gotam Singh Lalotra (17542062) Ponnusamy Sasikala (17542065) Dharmendra Singh Rajput (17542068) Rajesh Kaluri (17541486) Kuruva Lakshmanna (17542071) Mohammad Shorfuzzaman (17542050) Abdulmajeed Alsufyani (276154) Mueen Uddin (4903510) |
| author2_role | author author author author author author author author |
| author_facet | Vinod Kumar (48743) Gotam Singh Lalotra (17542062) Ponnusamy Sasikala (17542065) Dharmendra Singh Rajput (17542068) Rajesh Kaluri (17541486) Kuruva Lakshmanna (17542071) Mohammad Shorfuzzaman (17542050) Abdulmajeed Alsufyani (276154) Mueen Uddin (4903510) |
| author_role | author |
| dc.creator.none.fl_str_mv | Vinod Kumar (48743) Gotam Singh Lalotra (17542062) Ponnusamy Sasikala (17542065) Dharmendra Singh Rajput (17542068) Rajesh Kaluri (17541486) Kuruva Lakshmanna (17542071) Mohammad Shorfuzzaman (17542050) Abdulmajeed Alsufyani (276154) Mueen Uddin (4903510) |
| dc.date.none.fl_str_mv | 2022-07-13T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.3390/healthcare10071293 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Addressing_Binary_Classification_over_Class_Imbalanced_Clinical_Datasets_Using_Computationally_Intelligent_Techniques/24717522 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Health sciences Health services and systems Information and computing sciences Artificial intelligence Data management and data science Machine learning classification balancing techniques clinical dataset machine learning |
| dc.title.none.fl_str_mv | Addressing Binary Classification over Class Imbalanced Clinical Datasets Using Computationally Intelligent Techniques |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Nowadays, healthcare is the prime need of every human being in the world, and clinical datasets play an important role in developing an intelligent healthcare system for monitoring the health of people. Mostly, the real-world datasets are inherently class imbalanced, clinical datasets also suffer from this imbalance problem, and the imbalanced class distributions pose several issues in the training of classifiers. Consequently, classifiers suffer from low accuracy, precision, recall, and a high degree of misclassification, etc. We performed a brief literature review on the class imbalanced learning scenario. This study carries the empirical performance evaluation of six classifiers, namely Decision Tree, k-Nearest Neighbor, Logistic regression, Artificial Neural Network, Support Vector Machine, and Gaussian Naïve Bayes, over five imbalanced clinical datasets, Breast Cancer Disease, Coronary Heart Disease, Indian Liver Patient, Pima Indians Diabetes Database, and Coronary Kidney Disease, with respect to seven different class balancing techniques, namely Undersampling, Random oversampling, SMOTE, ADASYN, SVM-SMOTE, SMOTEEN, and SMOTETOMEK. In addition to this, the appropriate explanations for the superiority of the classifiers as well as data-balancing techniques are also explored. Furthermore, we discuss the possible recommendations on how to tackle the class imbalanced datasets while training the different supervised machine learning methods. Result analysis demonstrates that SMOTEEN balancing method often performed better over all the other six data-balancing techniques with all six classifiers and for all five clinical datasets. Except for SMOTEEN, all other six balancing techniques almost had equal performance but moderately lesser performance than SMOTEEN.</p><h2>Other Information</h2><p dir="ltr">Published in: Healthcare<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/healthcare10071293" target="_blank">https://dx.doi.org/10.3390/healthcare10071293</a></p><p dir="ltr">Disclaimer: The University of Doha for Science and Technology replaced the now-former College of the North Atlantic-Qatar after an Amiri decision in 2022. UDST has become and first national applied University in Qatar; it is also second national University in the country.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_9306360a964189071276cd27a035c049 |
| identifier_str_mv | 10.3390/healthcare10071293 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24717522 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Addressing Binary Classification over Class Imbalanced Clinical Datasets Using Computationally Intelligent TechniquesVinod Kumar (48743)Gotam Singh Lalotra (17542062)Ponnusamy Sasikala (17542065)Dharmendra Singh Rajput (17542068)Rajesh Kaluri (17541486)Kuruva Lakshmanna (17542071)Mohammad Shorfuzzaman (17542050)Abdulmajeed Alsufyani (276154)Mueen Uddin (4903510)Health sciencesHealth services and systemsInformation and computing sciencesArtificial intelligenceData management and data scienceMachine learningclassificationbalancing techniquesclinical datasetmachine learning<p dir="ltr">Nowadays, healthcare is the prime need of every human being in the world, and clinical datasets play an important role in developing an intelligent healthcare system for monitoring the health of people. Mostly, the real-world datasets are inherently class imbalanced, clinical datasets also suffer from this imbalance problem, and the imbalanced class distributions pose several issues in the training of classifiers. Consequently, classifiers suffer from low accuracy, precision, recall, and a high degree of misclassification, etc. We performed a brief literature review on the class imbalanced learning scenario. This study carries the empirical performance evaluation of six classifiers, namely Decision Tree, k-Nearest Neighbor, Logistic regression, Artificial Neural Network, Support Vector Machine, and Gaussian Naïve Bayes, over five imbalanced clinical datasets, Breast Cancer Disease, Coronary Heart Disease, Indian Liver Patient, Pima Indians Diabetes Database, and Coronary Kidney Disease, with respect to seven different class balancing techniques, namely Undersampling, Random oversampling, SMOTE, ADASYN, SVM-SMOTE, SMOTEEN, and SMOTETOMEK. In addition to this, the appropriate explanations for the superiority of the classifiers as well as data-balancing techniques are also explored. Furthermore, we discuss the possible recommendations on how to tackle the class imbalanced datasets while training the different supervised machine learning methods. Result analysis demonstrates that SMOTEEN balancing method often performed better over all the other six data-balancing techniques with all six classifiers and for all five clinical datasets. Except for SMOTEEN, all other six balancing techniques almost had equal performance but moderately lesser performance than SMOTEEN.</p><h2>Other Information</h2><p dir="ltr">Published in: Healthcare<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/healthcare10071293" target="_blank">https://dx.doi.org/10.3390/healthcare10071293</a></p><p dir="ltr">Disclaimer: The University of Doha for Science and Technology replaced the now-former College of the North Atlantic-Qatar after an Amiri decision in 2022. UDST has become and first national applied University in Qatar; it is also second national University in the country.</p>2022-07-13T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.3390/healthcare10071293https://figshare.com/articles/journal_contribution/Addressing_Binary_Classification_over_Class_Imbalanced_Clinical_Datasets_Using_Computationally_Intelligent_Techniques/24717522CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/247175222022-07-13T03:00:00Z |
| spellingShingle | Addressing Binary Classification over Class Imbalanced Clinical Datasets Using Computationally Intelligent Techniques Vinod Kumar (48743) Health sciences Health services and systems Information and computing sciences Artificial intelligence Data management and data science Machine learning classification balancing techniques clinical dataset machine learning |
| status_str | publishedVersion |
| title | Addressing Binary Classification over Class Imbalanced Clinical Datasets Using Computationally Intelligent Techniques |
| title_full | Addressing Binary Classification over Class Imbalanced Clinical Datasets Using Computationally Intelligent Techniques |
| title_fullStr | Addressing Binary Classification over Class Imbalanced Clinical Datasets Using Computationally Intelligent Techniques |
| title_full_unstemmed | Addressing Binary Classification over Class Imbalanced Clinical Datasets Using Computationally Intelligent Techniques |
| title_short | Addressing Binary Classification over Class Imbalanced Clinical Datasets Using Computationally Intelligent Techniques |
| title_sort | Addressing Binary Classification over Class Imbalanced Clinical Datasets Using Computationally Intelligent Techniques |
| topic | Health sciences Health services and systems Information and computing sciences Artificial intelligence Data management and data science Machine learning classification balancing techniques clinical dataset machine learning |