Enhancing Decision-Making and Data Management in Healthcare: A Hybrid Ensemble Learning and Blockchain Approach
Currently, big data is considered one of the most significant areas of research and development. The advancement in technologies along with the involvement of intelligent and automated devices in each field of development leads to huge generation, analysis, and the recording of information in the ne...
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MDPI AG
2025-01-01
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| Series: | Technologies |
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| Online Access: | https://www.mdpi.com/2227-7080/13/2/43 |
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| author | Geetanjali Rathee Razi Iqbal |
| author_facet | Geetanjali Rathee Razi Iqbal |
| author_sort | Geetanjali Rathee |
| collection | DOAJ |
| description | Currently, big data is considered one of the most significant areas of research and development. The advancement in technologies along with the involvement of intelligent and automated devices in each field of development leads to huge generation, analysis, and the recording of information in the network. Though a number of schemes have been proposed for providing accurate decision-making while analyzing the records, however, the existing methods lead to massive delays and difficulty in the management of stored information. Furthermore, the excessive delays in information processing pose a critical challenge to making accurate decisions in the context of big data. The aim of this paper is to propose an effective approach for accurate decision-making and analysis of the vast volumes of data generated by intelligent devices in the healthcare sector. The processed and managed records can be stored and accessed in a systematic and efficient manner. The proposed mechanism uses the hybrid of ensemble learning along with blockchain for fast and continuous recording and surveillance of information. The recorded information is analyzed using several existing methods focusing on various measurement outcomes. The results of the proposed technique are compared with existing techniques through various experiments that demonstrate the efficiency and accuracy of this technique. |
| format | Article |
| id | doaj-art-27e7b49604fe402d92b978b893d2a860 |
| institution | DOAJ |
| issn | 2227-7080 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Technologies |
| spelling | doaj-art-27e7b49604fe402d92b978b893d2a8602025-08-20T03:12:07ZengMDPI AGTechnologies2227-70802025-01-011324310.3390/technologies13020043Enhancing Decision-Making and Data Management in Healthcare: A Hybrid Ensemble Learning and Blockchain ApproachGeetanjali Rathee0Razi Iqbal1Department of Computer Science and Engineering, Netaji Subhas University of Technology, Dwarka Sector-3, New Delhi 110078, IndiaDepartment of Computer Science, Central Michigan University, Mt Pleasant, MI 48859, USACurrently, big data is considered one of the most significant areas of research and development. The advancement in technologies along with the involvement of intelligent and automated devices in each field of development leads to huge generation, analysis, and the recording of information in the network. Though a number of schemes have been proposed for providing accurate decision-making while analyzing the records, however, the existing methods lead to massive delays and difficulty in the management of stored information. Furthermore, the excessive delays in information processing pose a critical challenge to making accurate decisions in the context of big data. The aim of this paper is to propose an effective approach for accurate decision-making and analysis of the vast volumes of data generated by intelligent devices in the healthcare sector. The processed and managed records can be stored and accessed in a systematic and efficient manner. The proposed mechanism uses the hybrid of ensemble learning along with blockchain for fast and continuous recording and surveillance of information. The recorded information is analyzed using several existing methods focusing on various measurement outcomes. The results of the proposed technique are compared with existing techniques through various experiments that demonstrate the efficiency and accuracy of this technique.https://www.mdpi.com/2227-7080/13/2/43ensemble learningdecision-makingdata sciencebig datasecurityinformation management |
| spellingShingle | Geetanjali Rathee Razi Iqbal Enhancing Decision-Making and Data Management in Healthcare: A Hybrid Ensemble Learning and Blockchain Approach Technologies ensemble learning decision-making data science big data security information management |
| title | Enhancing Decision-Making and Data Management in Healthcare: A Hybrid Ensemble Learning and Blockchain Approach |
| title_full | Enhancing Decision-Making and Data Management in Healthcare: A Hybrid Ensemble Learning and Blockchain Approach |
| title_fullStr | Enhancing Decision-Making and Data Management in Healthcare: A Hybrid Ensemble Learning and Blockchain Approach |
| title_full_unstemmed | Enhancing Decision-Making and Data Management in Healthcare: A Hybrid Ensemble Learning and Blockchain Approach |
| title_short | Enhancing Decision-Making and Data Management in Healthcare: A Hybrid Ensemble Learning and Blockchain Approach |
| title_sort | enhancing decision making and data management in healthcare a hybrid ensemble learning and blockchain approach |
| topic | ensemble learning decision-making data science big data security information management |
| url | https://www.mdpi.com/2227-7080/13/2/43 |
| work_keys_str_mv | AT geetanjalirathee enhancingdecisionmakinganddatamanagementinhealthcareahybridensemblelearningandblockchainapproach AT raziiqbal enhancingdecisionmakinganddatamanagementinhealthcareahybridensemblelearningandblockchainapproach |