An Enhancement in Cancer Classification Accuracy Using a Two-Step Feature Selection Method Based on Artificial Neural Networks with 15 Neurons
An artificial neural network (ANN) is a tool that can be utilized to recognize cancer effectively. Nowadays, the risk of cancer is increasing dramatically all over the world. Detecting cancer is very difficult due to a lack of data. Proper data are essential for detecting cancer accurately. Cancer c...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-02-01
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| Series: | Symmetry |
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| Online Access: | https://www.mdpi.com/2073-8994/12/2/271 |
| _version_ | 1811186322941935616 |
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| author | Md Akizur Rahman Ravie Chandren Muniyandi |
| author_facet | Md Akizur Rahman Ravie Chandren Muniyandi |
| author_sort | Md Akizur Rahman |
| collection | DOAJ |
| description | An artificial neural network (ANN) is a tool that can be utilized to recognize cancer effectively. Nowadays, the risk of cancer is increasing dramatically all over the world. Detecting cancer is very difficult due to a lack of data. Proper data are essential for detecting cancer accurately. Cancer classification has been carried out by many researchers, but there is still a need to improve classification accuracy. For this purpose, in this research, a two-step feature selection (FS) technique with a 15-neuron neural network (NN), which classifies cancer with high accuracy, is proposed. The FS method is utilized to reduce feature attributes, and the 15-neuron network is utilized to classify the cancer. This research utilized the benchmark Wisconsin Diagnostic Breast Cancer (WDBC) dataset to compare the proposed method with other existing techniques, showing a significant improvement of up to 99.4% in classification accuracy. The results produced in this research are more promising and significant than those in existing papers. |
| first_indexed | 2024-04-11T13:43:36Z |
| format | Article |
| id | doaj.art-fa3a7782fc0242f69a4755a31a023c28 |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-04-11T13:43:36Z |
| publishDate | 2020-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-fa3a7782fc0242f69a4755a31a023c282022-12-22T04:21:10ZengMDPI AGSymmetry2073-89942020-02-0112227110.3390/sym12020271sym12020271An Enhancement in Cancer Classification Accuracy Using a Two-Step Feature Selection Method Based on Artificial Neural Networks with 15 NeuronsMd Akizur Rahman0Ravie Chandren Muniyandi1Research Center for Cyber Security, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, MalaysiaResearch Center for Cyber Security, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, MalaysiaAn artificial neural network (ANN) is a tool that can be utilized to recognize cancer effectively. Nowadays, the risk of cancer is increasing dramatically all over the world. Detecting cancer is very difficult due to a lack of data. Proper data are essential for detecting cancer accurately. Cancer classification has been carried out by many researchers, but there is still a need to improve classification accuracy. For this purpose, in this research, a two-step feature selection (FS) technique with a 15-neuron neural network (NN), which classifies cancer with high accuracy, is proposed. The FS method is utilized to reduce feature attributes, and the 15-neuron network is utilized to classify the cancer. This research utilized the benchmark Wisconsin Diagnostic Breast Cancer (WDBC) dataset to compare the proposed method with other existing techniques, showing a significant improvement of up to 99.4% in classification accuracy. The results produced in this research are more promising and significant than those in existing papers.https://www.mdpi.com/2073-8994/12/2/271artificial neural networkbest first searchcancer classificationfeature selectiontaguchi method |
| spellingShingle | Md Akizur Rahman Ravie Chandren Muniyandi An Enhancement in Cancer Classification Accuracy Using a Two-Step Feature Selection Method Based on Artificial Neural Networks with 15 Neurons Symmetry artificial neural network best first search cancer classification feature selection taguchi method |
| title | An Enhancement in Cancer Classification Accuracy Using a Two-Step Feature Selection Method Based on Artificial Neural Networks with 15 Neurons |
| title_full | An Enhancement in Cancer Classification Accuracy Using a Two-Step Feature Selection Method Based on Artificial Neural Networks with 15 Neurons |
| title_fullStr | An Enhancement in Cancer Classification Accuracy Using a Two-Step Feature Selection Method Based on Artificial Neural Networks with 15 Neurons |
| title_full_unstemmed | An Enhancement in Cancer Classification Accuracy Using a Two-Step Feature Selection Method Based on Artificial Neural Networks with 15 Neurons |
| title_short | An Enhancement in Cancer Classification Accuracy Using a Two-Step Feature Selection Method Based on Artificial Neural Networks with 15 Neurons |
| title_sort | enhancement in cancer classification accuracy using a two step feature selection method based on artificial neural networks with 15 neurons |
| topic | artificial neural network best first search cancer classification feature selection taguchi method |
| url | https://www.mdpi.com/2073-8994/12/2/271 |
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