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Hybrid of particle swarm optimization algorithm and fuzzy system for diabetes diagnosis | ||
| International Journal of Nonlinear Analysis and Applications | ||
| مقاله 4، دوره 15، شماره 2، اردیبهشت 2024، صفحه 39-46 اصل مقاله (948.18 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2022.29575.4196 | ||
| نویسندگان | ||
| Reza Ghabousian1؛ Yousef Farhang* 2؛ Kambiz Majidzadeh1؛ Amin Babazadeh Sangar1 | ||
| 1Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran | ||
| 2Department of Computer Engineering, Khoy Branch, Islamic Azad University, Khoy, Iran | ||
| تاریخ دریافت: 20 تیر 1401، تاریخ بازنگری: 25 اسفند 1400، تاریخ پذیرش: 30 آذر 1401 | ||
| چکیده | ||
| Diabetes is a dangerous disease in which the body is incapable of controlling blood sugar due to inadequate insulin hormone levels. This chronic disease increases blood sugar in patients. Therefore, if it is not controlled, it will cause many complications. A considerable number of people in the world suffer from this disease owing to its damage and lack of its initial diagnosis. The patient visits the doctor frequently to diagnose his/her illness and conducts various tests that are boring and costly. Increasing machine learning approaches through heuristics, and novel methods can somewhat decrease the problems. The current study aims to propose a model that can predict diabetes in patients with high accuracy. The paper introduces a new method based on the assortment of metaheuristic algorithms of a particle swarm and fuzzy inference system. The proposed method utilizes fuzzy systems to binary the particle swarm algorithm. The achieved model is applied to the diabetes dataset and then evaluated using a neural network classifier. The results indicate an increase in classification accuracy to 95.47% compared to other existing methods. | ||
| کلیدواژهها | ||
| Diabetes؛ PSO algorithm؛ neural networks؛ fuzzy systems؛ meta-heuristic algorithms | ||
| مراجع | ||
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