Comparative Analysis of Supervised Machine and Deep Learning Algorithms for Kyphosis Disease Detection
<p dir="ltr">Although Kyphosis, an excessive forward rounding of the upper back, can occur at any age, adolescence is the most common time for Kyphosis. Surgery is frequently performed on Kyphosis patients; however, the condition may persist after the operation. The tricky part is fi...
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| منشور في: |
2023
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| _version_ | 1864513507078176768 |
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| author | Alok Singh Chauhan (8264985) |
| author2 | Umesh Kumar Lilhore (17727684) Amit Kumar Gupta (703419) Poongodi Manoharan (17727687) Ruchi Rani Garg (19483039) Fahima Hajjej (11675462) Ismail Keshta (17727699) Kaamran Raahemifar (707645) |
| author2_role | author author author author author author author |
| author_facet | Alok Singh Chauhan (8264985) Umesh Kumar Lilhore (17727684) Amit Kumar Gupta (703419) Poongodi Manoharan (17727687) Ruchi Rani Garg (19483039) Fahima Hajjej (11675462) Ismail Keshta (17727699) Kaamran Raahemifar (707645) |
| author_role | author |
| dc.creator.none.fl_str_mv | Alok Singh Chauhan (8264985) Umesh Kumar Lilhore (17727684) Amit Kumar Gupta (703419) Poongodi Manoharan (17727687) Ruchi Rani Garg (19483039) Fahima Hajjej (11675462) Ismail Keshta (17727699) Kaamran Raahemifar (707645) |
| dc.date.none.fl_str_mv | 2023-04-17T09:00:00Z |
| dc.identifier.none.fl_str_mv | 10.3390/app13085012 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Comparative_Analysis_of_Supervised_Machine_and_Deep_Learning_Algorithms_for_Kyphosis_Disease_Detection/26830882 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biomedical and clinical sciences Clinical sciences Health sciences Health services and systems Information and computing sciences Machine learning kyphosis machine learning deep learning logistic regression Naïve Bayes random forest K-nearest neighbors support vector machine deep neural network |
| dc.title.none.fl_str_mv | Comparative Analysis of Supervised Machine and Deep Learning Algorithms for Kyphosis Disease Detection |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Although Kyphosis, an excessive forward rounding of the upper back, can occur at any age, adolescence is the most common time for Kyphosis. Surgery is frequently performed on Kyphosis patients; however, the condition may persist after the operation. The tricky part is figuring out, based on the patient’s traits, if the Kyphosis condition will continue after the treatment. There have been numerous models employed in the past to predict the Kyphosis disease, including Logistic Regression (LR), Naive Bayes (NB), Random Forest (RF), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Deep Neural Network (DNN), and others. Unfortunately, the precision was overestimated. Based on the dataset received from Kaggle, we investigated how to predict Kyphosis disorders more accurately by using these models with Hyperparameter tuning. While the calculations were being performed, certain variables were modified. The accuracy was increased by optimizing the fit parameters based on Hyperparameter tuning. Accuracy, recall or sensitivity, specificity, precision, balanced accuracy score, F1 score, and AUC-ROC score of all models, including the Hyperparameter tuning, were compared. Overall, the Hyperparameter-tuned DNN models excelled over the other models. The DNN models’ accuracy was 87.72% with 5-fold cross-validation and 87.64% with 10-fold cross-validation. It is advised that when a patient has a clinical procedure, the DNN model be trained to detect and foresee Kyphosis disease. Medical experts can use this study’s findings to correctly predict if a patient will still have Kyphosis after surgery. We propose that deep learning should be adopted and utilized as a crucial and necessary tool throughout the broad range of resolving biological queries.</p><h2>Other Information</h2><p dir="ltr">Published in: Applied Sciences<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/app13085012" target="_blank">https://dx.doi.org/10.3390/app13085012</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_f251f9925b63be8822345f1ab0bc481e |
| identifier_str_mv | 10.3390/app13085012 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/26830882 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Comparative Analysis of Supervised Machine and Deep Learning Algorithms for Kyphosis Disease DetectionAlok Singh Chauhan (8264985)Umesh Kumar Lilhore (17727684)Amit Kumar Gupta (703419)Poongodi Manoharan (17727687)Ruchi Rani Garg (19483039)Fahima Hajjej (11675462)Ismail Keshta (17727699)Kaamran Raahemifar (707645)Biomedical and clinical sciencesClinical sciencesHealth sciencesHealth services and systemsInformation and computing sciencesMachine learningkyphosismachine learningdeep learninglogistic regressionNaïve Bayesrandom forestK-nearest neighborssupport vector machinedeep neural network<p dir="ltr">Although Kyphosis, an excessive forward rounding of the upper back, can occur at any age, adolescence is the most common time for Kyphosis. Surgery is frequently performed on Kyphosis patients; however, the condition may persist after the operation. The tricky part is figuring out, based on the patient’s traits, if the Kyphosis condition will continue after the treatment. There have been numerous models employed in the past to predict the Kyphosis disease, including Logistic Regression (LR), Naive Bayes (NB), Random Forest (RF), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Deep Neural Network (DNN), and others. Unfortunately, the precision was overestimated. Based on the dataset received from Kaggle, we investigated how to predict Kyphosis disorders more accurately by using these models with Hyperparameter tuning. While the calculations were being performed, certain variables were modified. The accuracy was increased by optimizing the fit parameters based on Hyperparameter tuning. Accuracy, recall or sensitivity, specificity, precision, balanced accuracy score, F1 score, and AUC-ROC score of all models, including the Hyperparameter tuning, were compared. Overall, the Hyperparameter-tuned DNN models excelled over the other models. The DNN models’ accuracy was 87.72% with 5-fold cross-validation and 87.64% with 10-fold cross-validation. It is advised that when a patient has a clinical procedure, the DNN model be trained to detect and foresee Kyphosis disease. Medical experts can use this study’s findings to correctly predict if a patient will still have Kyphosis after surgery. We propose that deep learning should be adopted and utilized as a crucial and necessary tool throughout the broad range of resolving biological queries.</p><h2>Other Information</h2><p dir="ltr">Published in: Applied Sciences<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/app13085012" target="_blank">https://dx.doi.org/10.3390/app13085012</a></p>2023-04-17T09:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.3390/app13085012https://figshare.com/articles/journal_contribution/Comparative_Analysis_of_Supervised_Machine_and_Deep_Learning_Algorithms_for_Kyphosis_Disease_Detection/26830882CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/268308822023-04-17T09:00:00Z |
| spellingShingle | Comparative Analysis of Supervised Machine and Deep Learning Algorithms for Kyphosis Disease Detection Alok Singh Chauhan (8264985) Biomedical and clinical sciences Clinical sciences Health sciences Health services and systems Information and computing sciences Machine learning kyphosis machine learning deep learning logistic regression Naïve Bayes random forest K-nearest neighbors support vector machine deep neural network |
| status_str | publishedVersion |
| title | Comparative Analysis of Supervised Machine and Deep Learning Algorithms for Kyphosis Disease Detection |
| title_full | Comparative Analysis of Supervised Machine and Deep Learning Algorithms for Kyphosis Disease Detection |
| title_fullStr | Comparative Analysis of Supervised Machine and Deep Learning Algorithms for Kyphosis Disease Detection |
| title_full_unstemmed | Comparative Analysis of Supervised Machine and Deep Learning Algorithms for Kyphosis Disease Detection |
| title_short | Comparative Analysis of Supervised Machine and Deep Learning Algorithms for Kyphosis Disease Detection |
| title_sort | Comparative Analysis of Supervised Machine and Deep Learning Algorithms for Kyphosis Disease Detection |
| topic | Biomedical and clinical sciences Clinical sciences Health sciences Health services and systems Information and computing sciences Machine learning kyphosis machine learning deep learning logistic regression Naïve Bayes random forest K-nearest neighbors support vector machine deep neural network |