Hybrid TABU search with SDS based feature selection for lung cancer prediction
Cancer falls under a group of diseases where abnormal growths of the cells are observed. Generally, lung cancer does not result in any type of obvious symptoms in its early stages. Among the people diagnosed with lung cancer, about 40% are found to be in an advanced stage. Thus, the motivation of th...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2022-01-01
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| Series: | International Journal of Intelligent Networks |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S266660302200015X |
| _version_ | 1798016164306092032 |
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| author | S. Shanthi V.S. Akshaya J.A. Smitha M. Bommy |
| author_facet | S. Shanthi V.S. Akshaya J.A. Smitha M. Bommy |
| author_sort | S. Shanthi |
| collection | DOAJ |
| description | Cancer falls under a group of diseases where abnormal growths of the cells are observed. Generally, lung cancer does not result in any type of obvious symptoms in its early stages. Among the people diagnosed with lung cancer, about 40% are found to be in an advanced stage. Thus, the motivation of the work is to present an automatic screening of lung images for early diagnosis. For this, Machine Learning (ML) methods are popularly employed as a tool among medical researchers for classifying their medical images. To improve the performance of Lung cancer detection with ML techniques, feature selection is employed. As the feature selection is a Nondeterministic Polynomial (NP) hard problem, metaheuristic algorithms are widely used for finding the optimal feature set. The Tabu Search (TS) is semi-deterministic and also tends to act as a method of local, as well as global search. The techniques are capable of discovering and further identifying the relationships and patterns among them obtained from complex datasets and are also capable of effective prediction. In this work, a new hybrid TS with Stochastic Diffusion Search (SDS) based feature selection that was employed using the Naïve Bayes, Decision tree and Neural Network (NN) classifiers to improve classification. The results demonstrate the effectiveness of the proposed TABU-SDS- NN which achieves an accuracy of 94.07%. |
| first_indexed | 2024-04-11T15:46:23Z |
| format | Article |
| id | doaj.art-fa59b3a7783147fc9df8ad4f6152e97b |
| institution | Directory Open Access Journal |
| issn | 2666-6030 |
| language | English |
| last_indexed | 2024-04-11T15:46:23Z |
| publishDate | 2022-01-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | International Journal of Intelligent Networks |
| spelling | doaj.art-fa59b3a7783147fc9df8ad4f6152e97b2022-12-22T04:15:31ZengKeAi Communications Co., Ltd.International Journal of Intelligent Networks2666-60302022-01-013143149Hybrid TABU search with SDS based feature selection for lung cancer predictionS. Shanthi0V.S. Akshaya1J.A. Smitha2M. Bommy3Department of CST, Madanapalle Institute of Technology and Science, Madanapalle, Andhra Pradesh, 517325, India; Corresponding author.Department of CSE, Sri Eshwar College of Engineering, Coimbatore, Tamilnadu, 641202, IndiaDepartment of CSE, Sri Sairam College of Engineering, Anekal, Bangalore, IndiaDepartment of CSE, Madanapalle Institute of Technology and Science, Madanapalle, Andhra Pradesh, 517325, IndiaCancer falls under a group of diseases where abnormal growths of the cells are observed. Generally, lung cancer does not result in any type of obvious symptoms in its early stages. Among the people diagnosed with lung cancer, about 40% are found to be in an advanced stage. Thus, the motivation of the work is to present an automatic screening of lung images for early diagnosis. For this, Machine Learning (ML) methods are popularly employed as a tool among medical researchers for classifying their medical images. To improve the performance of Lung cancer detection with ML techniques, feature selection is employed. As the feature selection is a Nondeterministic Polynomial (NP) hard problem, metaheuristic algorithms are widely used for finding the optimal feature set. The Tabu Search (TS) is semi-deterministic and also tends to act as a method of local, as well as global search. The techniques are capable of discovering and further identifying the relationships and patterns among them obtained from complex datasets and are also capable of effective prediction. In this work, a new hybrid TS with Stochastic Diffusion Search (SDS) based feature selection that was employed using the Naïve Bayes, Decision tree and Neural Network (NN) classifiers to improve classification. The results demonstrate the effectiveness of the proposed TABU-SDS- NN which achieves an accuracy of 94.07%.http://www.sciencedirect.com/science/article/pii/S266660302200015XLung cancerFeature selectionMachine learning (ML)Tabu search (TS)Stochastic diffusion search (SDS)Decision tree |
| spellingShingle | S. Shanthi V.S. Akshaya J.A. Smitha M. Bommy Hybrid TABU search with SDS based feature selection for lung cancer prediction International Journal of Intelligent Networks Lung cancer Feature selection Machine learning (ML) Tabu search (TS) Stochastic diffusion search (SDS) Decision tree |
| title | Hybrid TABU search with SDS based feature selection for lung cancer prediction |
| title_full | Hybrid TABU search with SDS based feature selection for lung cancer prediction |
| title_fullStr | Hybrid TABU search with SDS based feature selection for lung cancer prediction |
| title_full_unstemmed | Hybrid TABU search with SDS based feature selection for lung cancer prediction |
| title_short | Hybrid TABU search with SDS based feature selection for lung cancer prediction |
| title_sort | hybrid tabu search with sds based feature selection for lung cancer prediction |
| topic | Lung cancer Feature selection Machine learning (ML) Tabu search (TS) Stochastic diffusion search (SDS) Decision tree |
| url | http://www.sciencedirect.com/science/article/pii/S266660302200015X |
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