Enhancing Support Vector Classification for Diabetes Prediction with Novel Optimization Algorithms of Intelligent Health Services
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| Title: | Enhancing Support Vector Classification for Diabetes Prediction with Novel Optimization Algorithms of Intelligent Health Services |
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| Authors: | Debojani Paul Chowdhury, Aditi Paul Chowdhury, Apurba Das, Pinki Pinki |
| Source: | Advances in Engineering and Intelligence Systems, Vol 004, Iss 02, Pp 35-47 (2025) |
| Publisher Information: | Bilijipub publisher, 2025. |
| Publication Year: | 2025 |
| Collection: | LCC:Computer software LCC:Mining engineering. Metallurgy |
| Subject Terms: | diabetes, support vector classification, quadratic interpolation optimizer, african vulture optimization algorithm, tunicate swarm algorithm, Computer software, QA76.75-76.765, Mining engineering. Metallurgy, TN1-997 |
| Description: | One of the most serious medical illnesses impacting a large number of people globally is diabetes mellitus. This condition is influenced by several factors, including advanced age, obesity, sedentary lifestyle, hereditary susceptibility, bad eating habits, and high blood pressure. Diabetes patients are more vulnerable to heart disease, renal failure, stroke, visual impairment, and nerve damage, among other consequences. In modern hospital settings, diagnosing diabetes entails gathering a wealth of relevant data via a series of tests, allowing medical practitioners to customize treatment regimens. In this regard, integrating big data analytics has become an essential tool for the healthcare industry. Healthcare firms use big data analytics to dive into large datasets and find hidden patterns and insights since they have access to enormous amounts of data. This analytical skill enables practitioners to extract meaningful insights from the data, which supports well-informed decision-making and accurate result prediction. Support Vector Classification (SVC) was employed to predict Diabetes in this study. Additionally, 3 novel metaheuristic algorithms, Quadratic Interpolation Optimizer (QIO), Tunicate Swarm Algorithm (TSA), and African Vulture Optimization Algorithm (AVOA) were utilized to enhance the SVC’s performance. The fundamental model was combined with 3 optimization approaches to create the hybrid models: SVC + QIO (SVQI), SVC + AVOA (SVAV), and SVC + TSA (SVTS). When it came to accuracy metric values during testing, the SVQI model performed best with a value of 0.877. The SVAV and SVTS models both secured the second-best performance in this segment with values of 0.871. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 2821-0263 |
| Relation: | https://aeis.bilijipub.com/article_223843_5587b28e91c5c9ada868c83e23dc2fb9.pdf; https://doaj.org/toc/2821-0263 |
| DOI: | 10.22034/aeis.2025.515310.1305 |
| Access URL: | https://doaj.org/article/3d00ff1a55164f0389d52fb22a719cb8 |
| Accession Number: | edsdoj.3d00ff1a55164f0389d52fb22a719cb8 |
| Database: | Directory of Open Access Journals |
Nájsť tento článok vo Web of Science | Abstract: | One of the most serious medical illnesses impacting a large number of people globally is diabetes mellitus. This condition is influenced by several factors, including advanced age, obesity, sedentary lifestyle, hereditary susceptibility, bad eating habits, and high blood pressure. Diabetes patients are more vulnerable to heart disease, renal failure, stroke, visual impairment, and nerve damage, among other consequences. In modern hospital settings, diagnosing diabetes entails gathering a wealth of relevant data via a series of tests, allowing medical practitioners to customize treatment regimens. In this regard, integrating big data analytics has become an essential tool for the healthcare industry. Healthcare firms use big data analytics to dive into large datasets and find hidden patterns and insights since they have access to enormous amounts of data. This analytical skill enables practitioners to extract meaningful insights from the data, which supports well-informed decision-making and accurate result prediction. Support Vector Classification (SVC) was employed to predict Diabetes in this study. Additionally, 3 novel metaheuristic algorithms, Quadratic Interpolation Optimizer (QIO), Tunicate Swarm Algorithm (TSA), and African Vulture Optimization Algorithm (AVOA) were utilized to enhance the SVC’s performance. The fundamental model was combined with 3 optimization approaches to create the hybrid models: SVC + QIO (SVQI), SVC + AVOA (SVAV), and SVC + TSA (SVTS). When it came to accuracy metric values during testing, the SVQI model performed best with a value of 0.877. The SVAV and SVTS models both secured the second-best performance in this segment with values of 0.871. |
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| ISSN: | 28210263 |
| DOI: | 10.22034/aeis.2025.515310.1305 |