A recommender system with multi-objective hybrid Harris Hawk optimization for feature selection and disease diagnosis
This study proposes a health recommender system to analyze health risk and disease prediction by identifying the most responsible disease-causing factors using a hybrid Genetic–Harris Hawk optimization multi-objective feature selection approach. The proposed recommender system uses the Tree-based Pi...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | Healthcare Analytics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772442525000036 |
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| author | Madhusree Kuanr Puspanjali Mohapatra |
| author_facet | Madhusree Kuanr Puspanjali Mohapatra |
| author_sort | Madhusree Kuanr |
| collection | DOAJ |
| description | This study proposes a health recommender system to analyze health risk and disease prediction by identifying the most responsible disease-causing factors using a hybrid Genetic–Harris Hawk optimization multi-objective feature selection approach. The proposed recommender system uses the Tree-based Pipeline Optimization Tool (TPOT) automated machine learning model to recommend the most suitable machine learning prediction model with the best classifier in terms of classification accuracy for a disease with the selected features. It also recommends the top three disease-causing features for a particular disease that can be utilized to analyze a person’s health risk. The proposed system has also been compared with the competing prediction approaches using Principal Component Analysis (PCA), Singular Vector Decomposition (SVD), and Autoencoders. We show that the proposed system outperforms competing approaches in terms of classification accuracy. |
| format | Article |
| id | doaj-art-9bbd7e1e0fd0493ea8051fc4713dd909 |
| institution | Kabale University |
| issn | 2772-4425 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Healthcare Analytics |
| spelling | doaj-art-9bbd7e1e0fd0493ea8051fc4713dd9092025-02-05T04:32:50ZengElsevierHealthcare Analytics2772-44252025-06-017100384A recommender system with multi-objective hybrid Harris Hawk optimization for feature selection and disease diagnosisMadhusree Kuanr0Puspanjali Mohapatra1Corresponding author.; Department of Computer Science and Engineering, IIIT Bhubaneswar, Odisha 751003, IndiaDepartment of Computer Science and Engineering, IIIT Bhubaneswar, Odisha 751003, IndiaThis study proposes a health recommender system to analyze health risk and disease prediction by identifying the most responsible disease-causing factors using a hybrid Genetic–Harris Hawk optimization multi-objective feature selection approach. The proposed recommender system uses the Tree-based Pipeline Optimization Tool (TPOT) automated machine learning model to recommend the most suitable machine learning prediction model with the best classifier in terms of classification accuracy for a disease with the selected features. It also recommends the top three disease-causing features for a particular disease that can be utilized to analyze a person’s health risk. The proposed system has also been compared with the competing prediction approaches using Principal Component Analysis (PCA), Singular Vector Decomposition (SVD), and Autoencoders. We show that the proposed system outperforms competing approaches in terms of classification accuracy.http://www.sciencedirect.com/science/article/pii/S2772442525000036Recommender systemMulti-objective feature selectionAutomated machine learningPrincipal Component AnalysisSingular Vector DecompositionAutoencoder |
| spellingShingle | Madhusree Kuanr Puspanjali Mohapatra A recommender system with multi-objective hybrid Harris Hawk optimization for feature selection and disease diagnosis Healthcare Analytics Recommender system Multi-objective feature selection Automated machine learning Principal Component Analysis Singular Vector Decomposition Autoencoder |
| title | A recommender system with multi-objective hybrid Harris Hawk optimization for feature selection and disease diagnosis |
| title_full | A recommender system with multi-objective hybrid Harris Hawk optimization for feature selection and disease diagnosis |
| title_fullStr | A recommender system with multi-objective hybrid Harris Hawk optimization for feature selection and disease diagnosis |
| title_full_unstemmed | A recommender system with multi-objective hybrid Harris Hawk optimization for feature selection and disease diagnosis |
| title_short | A recommender system with multi-objective hybrid Harris Hawk optimization for feature selection and disease diagnosis |
| title_sort | recommender system with multi objective hybrid harris hawk optimization for feature selection and disease diagnosis |
| topic | Recommender system Multi-objective feature selection Automated machine learning Principal Component Analysis Singular Vector Decomposition Autoencoder |
| url | http://www.sciencedirect.com/science/article/pii/S2772442525000036 |
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