Review of Feature Selection, Dimensionality Reduction and Classification for Chronic Disease Diagnosis
The early diagnosis of chronic diseases plays a vital role in the field of healthcare communities and biomedical, where it is necessary for detecting the disease at an initial phase to reduce the death rate. This paper investigates the use of feature selection, dimensionality reduction and classific...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9452069/ |
| _version_ | 1818438225742528512 |
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| author | Afnan M. Alhassan Wan Mohd Nazmee Wan Zainon |
| author_facet | Afnan M. Alhassan Wan Mohd Nazmee Wan Zainon |
| author_sort | Afnan M. Alhassan |
| collection | DOAJ |
| description | The early diagnosis of chronic diseases plays a vital role in the field of healthcare communities and biomedical, where it is necessary for detecting the disease at an initial phase to reduce the death rate. This paper investigates the use of feature selection, dimensionality reduction and classification techniques to predict and diagnose chronic disease. The appropriate selection of attributes plays a crucial role in improving the classification accuracy of the diagnosis systems. Additionally, dimensionality reduction techniques effectively improve the overall performance of the machine learning algorithms. On chronic disease databases, the classification techniques deliver efficient predictive results by developing intelligent, adaptive and automated system. Parallel and adaptive classification techniques are also analyzed in chronic disease diagnosis which is used to stimulate the classification procedure and to improve the computational cost and time. This survey article represents the overview of feature selection, dimensionality reduction and classification techniques and their inherent benefits and drawbacks. |
| first_indexed | 2024-12-14T17:37:11Z |
| format | Article |
| id | doaj.art-bb5274489ce04802b9f39f43855dd94f |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T17:37:11Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-bb5274489ce04802b9f39f43855dd94f2022-12-21T22:52:56ZengIEEEIEEE Access2169-35362021-01-019873108731710.1109/ACCESS.2021.30886139452069Review of Feature Selection, Dimensionality Reduction and Classification for Chronic Disease DiagnosisAfnan M. Alhassan0https://orcid.org/0000-0002-0414-5835Wan Mohd Nazmee Wan Zainon1School of Computer Science, Universiti Sains Malaysia, George Town, MalaysiaSchool of Computer Science, Universiti Sains Malaysia, George Town, MalaysiaThe early diagnosis of chronic diseases plays a vital role in the field of healthcare communities and biomedical, where it is necessary for detecting the disease at an initial phase to reduce the death rate. This paper investigates the use of feature selection, dimensionality reduction and classification techniques to predict and diagnose chronic disease. The appropriate selection of attributes plays a crucial role in improving the classification accuracy of the diagnosis systems. Additionally, dimensionality reduction techniques effectively improve the overall performance of the machine learning algorithms. On chronic disease databases, the classification techniques deliver efficient predictive results by developing intelligent, adaptive and automated system. Parallel and adaptive classification techniques are also analyzed in chronic disease diagnosis which is used to stimulate the classification procedure and to improve the computational cost and time. This survey article represents the overview of feature selection, dimensionality reduction and classification techniques and their inherent benefits and drawbacks.https://ieeexplore.ieee.org/document/9452069/Adaptive classificationchronic diseasedimensionality reductionfeature selectionparallel classification |
| spellingShingle | Afnan M. Alhassan Wan Mohd Nazmee Wan Zainon Review of Feature Selection, Dimensionality Reduction and Classification for Chronic Disease Diagnosis IEEE Access Adaptive classification chronic disease dimensionality reduction feature selection parallel classification |
| title | Review of Feature Selection, Dimensionality Reduction and Classification for Chronic Disease Diagnosis |
| title_full | Review of Feature Selection, Dimensionality Reduction and Classification for Chronic Disease Diagnosis |
| title_fullStr | Review of Feature Selection, Dimensionality Reduction and Classification for Chronic Disease Diagnosis |
| title_full_unstemmed | Review of Feature Selection, Dimensionality Reduction and Classification for Chronic Disease Diagnosis |
| title_short | Review of Feature Selection, Dimensionality Reduction and Classification for Chronic Disease Diagnosis |
| title_sort | review of feature selection dimensionality reduction and classification for chronic disease diagnosis |
| topic | Adaptive classification chronic disease dimensionality reduction feature selection parallel classification |
| url | https://ieeexplore.ieee.org/document/9452069/ |
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