Biomedical data analysis using neuro-fuzzy model with post-feature reduction
Now-a-days, a large volume of biomedical data are continuously generated from various biomedical devices and experiments due to the rapid technological advancement in medical science. The effective analysis of these biomedical data such as extracting the significant features biologically and diagnos...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022-06-01
|
| Series: | Journal of King Saud University: Computer and Information Sciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1319157819311656 |
| _version_ | 1811240491508826112 |
|---|---|
| author | Himansu Das Bighnaraj Naik H.S. Behera Shalini Jaiswal Priyanka Mahato Minakhi Rout |
| author_facet | Himansu Das Bighnaraj Naik H.S. Behera Shalini Jaiswal Priyanka Mahato Minakhi Rout |
| author_sort | Himansu Das |
| collection | DOAJ |
| description | Now-a-days, a large volume of biomedical data are continuously generated from various biomedical devices and experiments due to the rapid technological advancement in medical science. The effective analysis of these biomedical data such as extracting the significant features biologically and diagnostically is really a challenging task. This paper proposes a Neuro-Fuzzy model with post-feature reduction to analyze these complex biomedical data. The proposed Neuro-Fuzzy approach uses class belongingness fuzzification of input patterns to handle uncertainty issues. However, the complexity of the model increases due to this fuzzy expansion of input patterns. On the other hand, all the expanded fuzzified patterns may not always be significant for model identification. To address this issue, post-feature reduction has been employed on fuzzified patterns to filter out the irrelevant, redundant and noisy features. Unlike pre-feature reduction, this allows all the features to be participated in the fuzzification process and then identify irrelevant features from the fuzzified patterns. Further, this approach allows exploring potential fuzzified features from the strong as well as weak feature set. The effectiveness of this proposed model has been tested and validated with a variety of benchmark biomedical data collected from various domains. |
| first_indexed | 2024-04-12T13:21:14Z |
| format | Article |
| id | doaj.art-f0ee2dcad2fa4639afca03e4b62f5587 |
| institution | Directory Open Access Journal |
| issn | 1319-1578 |
| language | English |
| last_indexed | 2024-04-12T13:21:14Z |
| publishDate | 2022-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj.art-f0ee2dcad2fa4639afca03e4b62f55872022-12-22T03:31:29ZengElsevierJournal of King Saud University: Computer and Information Sciences1319-15782022-06-0134625402550Biomedical data analysis using neuro-fuzzy model with post-feature reductionHimansu Das0Bighnaraj Naik1H.S. Behera2Shalini Jaiswal3Priyanka Mahato4Minakhi Rout5Department of Information Technology, Veer Surendra Sai University of Technology, Burla, Sambalpur 768018, Odisha, India; Corresponding author.Department of Computer Application, Veer Surendra Sai University of Technology, Burla, Sambalpur 768018, Odisha, IndiaDepartment of Information Technology, Veer Surendra Sai University of Technology, Burla, Sambalpur 768018, Odisha, IndiaSchool of Computer Engineering, Kalinga Institute of Industrial Technology (KIIT) Deemed to be University, Bhubaneswar 751024, Odisha, IndiaSchool of Computer Engineering, Kalinga Institute of Industrial Technology (KIIT) Deemed to be University, Bhubaneswar 751024, Odisha, IndiaSchool of Computer Engineering, Kalinga Institute of Industrial Technology (KIIT) Deemed to be University, Bhubaneswar 751024, Odisha, IndiaNow-a-days, a large volume of biomedical data are continuously generated from various biomedical devices and experiments due to the rapid technological advancement in medical science. The effective analysis of these biomedical data such as extracting the significant features biologically and diagnostically is really a challenging task. This paper proposes a Neuro-Fuzzy model with post-feature reduction to analyze these complex biomedical data. The proposed Neuro-Fuzzy approach uses class belongingness fuzzification of input patterns to handle uncertainty issues. However, the complexity of the model increases due to this fuzzy expansion of input patterns. On the other hand, all the expanded fuzzified patterns may not always be significant for model identification. To address this issue, post-feature reduction has been employed on fuzzified patterns to filter out the irrelevant, redundant and noisy features. Unlike pre-feature reduction, this allows all the features to be participated in the fuzzification process and then identify irrelevant features from the fuzzified patterns. Further, this approach allows exploring potential fuzzified features from the strong as well as weak feature set. The effectiveness of this proposed model has been tested and validated with a variety of benchmark biomedical data collected from various domains.http://www.sciencedirect.com/science/article/pii/S1319157819311656Biomedical researchClassificationMachine learningDimensionality reductionFeature reductionNeuro-fuzzy |
| spellingShingle | Himansu Das Bighnaraj Naik H.S. Behera Shalini Jaiswal Priyanka Mahato Minakhi Rout Biomedical data analysis using neuro-fuzzy model with post-feature reduction Journal of King Saud University: Computer and Information Sciences Biomedical research Classification Machine learning Dimensionality reduction Feature reduction Neuro-fuzzy |
| title | Biomedical data analysis using neuro-fuzzy model with post-feature reduction |
| title_full | Biomedical data analysis using neuro-fuzzy model with post-feature reduction |
| title_fullStr | Biomedical data analysis using neuro-fuzzy model with post-feature reduction |
| title_full_unstemmed | Biomedical data analysis using neuro-fuzzy model with post-feature reduction |
| title_short | Biomedical data analysis using neuro-fuzzy model with post-feature reduction |
| title_sort | biomedical data analysis using neuro fuzzy model with post feature reduction |
| topic | Biomedical research Classification Machine learning Dimensionality reduction Feature reduction Neuro-fuzzy |
| url | http://www.sciencedirect.com/science/article/pii/S1319157819311656 |
| work_keys_str_mv | AT himansudas biomedicaldataanalysisusingneurofuzzymodelwithpostfeaturereduction AT bighnarajnaik biomedicaldataanalysisusingneurofuzzymodelwithpostfeaturereduction AT hsbehera biomedicaldataanalysisusingneurofuzzymodelwithpostfeaturereduction AT shalinijaiswal biomedicaldataanalysisusingneurofuzzymodelwithpostfeaturereduction AT priyankamahato biomedicaldataanalysisusingneurofuzzymodelwithpostfeaturereduction AT minakhirout biomedicaldataanalysisusingneurofuzzymodelwithpostfeaturereduction |