Machine Learning with Tunicate Swarm Optimization for Improved Disc Herniation Prediction
A common spinal ailment called disc herniation is the displacement of the spinal discs' inner core as a result of a rupture in their outer covering. The many variables that contribute to disc herniation are examined in this study, including age-related deterioration, genetic predispositions, li...
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Bilijipub publisher
2024-06-01
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| Series: | Advances in Engineering and Intelligence Systems |
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| Online Access: | https://aeis.bilijipub.com/article_199247_9b5fdad33d29647e7465a31802d476dd.pdf |
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| author | Theofilos Gofas Argyros Maris |
| author_facet | Theofilos Gofas Argyros Maris |
| author_sort | Theofilos Gofas |
| collection | DOAJ |
| description | A common spinal ailment called disc herniation is the displacement of the spinal discs' inner core as a result of a rupture in their outer covering. The many variables that contribute to disc herniation are examined in this study, including age-related deterioration, genetic predispositions, lifestyle choices, and traumatic experiences. This study highlights the need for accurate prognosis and therapeutic efficacy, and it promotes machine learning (ML) as an essential diagnostic tool. This study employs Extra Tree Classification (ETC), Naïve Bayes Classification (NBC), and Logistic Regression Classification (LRC) to forecast disk herniation. Additionally, Tunicate Swarm Optimization (TSO) is integrated to enhance the accuracy of all three models NBC, ETC, and LRC. To ensure impartiality, unbiased performance assessors are engaged to objectively evaluate the model outcomes. The study's findings showcase the effectiveness of the prediction model for disk herniation. Through hybridization with one optimizer, the three base models yield the following outputs: ETC + TSO (ETTS), NBC + TSO (NBTS), and LRC + TSO (LRTS). In the testing phase, the LRTS model exhibits the utmost performance, attaining an accuracy value of 0.957, while conversely, the NBC model demonstrates the least robust performance, yielding a value of 0.893. These findings unequivocally underscore the superior predictive capability of the LRTS model in forecasting discherniation. |
| format | Article |
| id | doaj-art-55e747966668457885a56bceb08ecd5b |
| institution | Kabale University |
| issn | 2821-0263 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | Bilijipub publisher |
| record_format | Article |
| series | Advances in Engineering and Intelligence Systems |
| spelling | doaj-art-55e747966668457885a56bceb08ecd5b2025-02-12T08:47:56ZengBilijipub publisherAdvances in Engineering and Intelligence Systems2821-02632024-06-010030212714210.22034/aeis.2024.456009.1192199247Machine Learning with Tunicate Swarm Optimization for Improved Disc Herniation PredictionTheofilos Gofas0Argyros Maris1Department of Mechanical Engineering and Aeronautics, Stochastic Mechanical Systems and Automation Laboratory, University of Patras, Patras, 26504, GreeceDepartment of Communication and Digital Media, University of Western Macedonia, Fourka, Kastoria, 52100, GreeceA common spinal ailment called disc herniation is the displacement of the spinal discs' inner core as a result of a rupture in their outer covering. The many variables that contribute to disc herniation are examined in this study, including age-related deterioration, genetic predispositions, lifestyle choices, and traumatic experiences. This study highlights the need for accurate prognosis and therapeutic efficacy, and it promotes machine learning (ML) as an essential diagnostic tool. This study employs Extra Tree Classification (ETC), Naïve Bayes Classification (NBC), and Logistic Regression Classification (LRC) to forecast disk herniation. Additionally, Tunicate Swarm Optimization (TSO) is integrated to enhance the accuracy of all three models NBC, ETC, and LRC. To ensure impartiality, unbiased performance assessors are engaged to objectively evaluate the model outcomes. The study's findings showcase the effectiveness of the prediction model for disk herniation. Through hybridization with one optimizer, the three base models yield the following outputs: ETC + TSO (ETTS), NBC + TSO (NBTS), and LRC + TSO (LRTS). In the testing phase, the LRTS model exhibits the utmost performance, attaining an accuracy value of 0.957, while conversely, the NBC model demonstrates the least robust performance, yielding a value of 0.893. These findings unequivocally underscore the superior predictive capability of the LRTS model in forecasting discherniation.https://aeis.bilijipub.com/article_199247_9b5fdad33d29647e7465a31802d476dd.pdfdisc herniationmachine learningextra tree classificationnaïve bayes classificationlogistic regression classificationtunicate swarm optimization |
| spellingShingle | Theofilos Gofas Argyros Maris Machine Learning with Tunicate Swarm Optimization for Improved Disc Herniation Prediction Advances in Engineering and Intelligence Systems disc herniation machine learning extra tree classification naïve bayes classification logistic regression classification tunicate swarm optimization |
| title | Machine Learning with Tunicate Swarm Optimization for Improved Disc Herniation Prediction |
| title_full | Machine Learning with Tunicate Swarm Optimization for Improved Disc Herniation Prediction |
| title_fullStr | Machine Learning with Tunicate Swarm Optimization for Improved Disc Herniation Prediction |
| title_full_unstemmed | Machine Learning with Tunicate Swarm Optimization for Improved Disc Herniation Prediction |
| title_short | Machine Learning with Tunicate Swarm Optimization for Improved Disc Herniation Prediction |
| title_sort | machine learning with tunicate swarm optimization for improved disc herniation prediction |
| topic | disc herniation machine learning extra tree classification naïve bayes classification logistic regression classification tunicate swarm optimization |
| url | https://aeis.bilijipub.com/article_199247_9b5fdad33d29647e7465a31802d476dd.pdf |
| work_keys_str_mv | AT theofilosgofas machinelearningwithtunicateswarmoptimizationforimproveddischerniationprediction AT argyrosmaris machinelearningwithtunicateswarmoptimizationforimproveddischerniationprediction |