Improve data classification performance in diagnosing diabetes using the Binary Exchange Market Algorithm
Abstract Today's lifestyle has led to a significant increase in referrals to medical centers to diagnose various diseases. To this end, over the past few years, researchers have turned to new diagnostic methods, including data mining and artificial intelligence, intending to facilitate the dete...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2022-04-01
|
| Series: | Journal of Big Data |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40537-022-00598-z |
| _version_ | 1828366673599528960 |
|---|---|
| author | Faranak Rezaei Maryam Abbasitabar Shirin Mirzaei Zahra Kamari Direh Sahar Ahmadi Zahra Azizi Darya Danialy |
| author_facet | Faranak Rezaei Maryam Abbasitabar Shirin Mirzaei Zahra Kamari Direh Sahar Ahmadi Zahra Azizi Darya Danialy |
| author_sort | Faranak Rezaei |
| collection | DOAJ |
| description | Abstract Today's lifestyle has led to a significant increase in referrals to medical centers to diagnose various diseases. To this end, over the past few years, researchers have turned to new diagnostic methods, including data mining and artificial intelligence, intending to facilitate the detection process and increase reliability. The high volume of data available in medical centers can be considered one of the main problems in using these methods. The optimal selection of essential and influential features reduces the maximum dimension for better diagnosis with more reliability of results. In this paper, a new approach uses a Binary Exchange Market Algorithm (BEMA) to identify essential and practical features in the diabetes dataset and determine the best algorithm binary function (type of sigmoid function) to improve the performance of the EMA algorithm is presented. For validation and efficiency of the proposed BEMA algorithm, several SVM, KNN, and NB classification models have been used to train and test the final model. The results obtained from the evaluations show that the proposed BEMA-SVM combined method has a better performance than the previous methods to improve accuracy and offer an effect equivalent to 98.502%. Also, to provide better results and more reliability than the proposed method, researchers can use a combination of several classes with the proposed method, which is outside the scope of this study. |
| first_indexed | 2024-04-14T05:49:06Z |
| format | Article |
| id | doaj.art-034798684d574ae7b133744d4f58fb77 |
| institution | Directory Open Access Journal |
| issn | 2196-1115 |
| language | English |
| last_indexed | 2024-04-14T05:49:06Z |
| publishDate | 2022-04-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Big Data |
| spelling | doaj.art-034798684d574ae7b133744d4f58fb772022-12-22T02:09:11ZengSpringerOpenJournal of Big Data2196-11152022-04-019111410.1186/s40537-022-00598-zImprove data classification performance in diagnosing diabetes using the Binary Exchange Market AlgorithmFaranak Rezaei0Maryam Abbasitabar1Shirin Mirzaei2Zahra Kamari Direh3Sahar Ahmadi4Zahra Azizi5Darya Danialy6Department of Electrical Engineering and Medical Equipment, Imam Reza Center of Applied Science and Technology (IRCAST)Department of Electrical Engineering and Medical Equipment, Imam Reza Center of Applied Science and Technology (IRCAST)Department of Electrical Engineering and Medical Equipment, Imam Reza Center of Applied Science and Technology (IRCAST)Department of Electrical Engineering and Medical Equipment, Imam Reza Center of Applied Science and Technology (IRCAST)Department of Electrical Engineering and Medical Equipment, Imam Reza Center of Applied Science and Technology (IRCAST)Department of Electrical Engineering and Medical Equipment, Imam Reza Center of Applied Science and Technology (IRCAST)Department of Electrical Engineering and Medical Equipment, Imam Reza Center of Applied Science and Technology (IRCAST)Abstract Today's lifestyle has led to a significant increase in referrals to medical centers to diagnose various diseases. To this end, over the past few years, researchers have turned to new diagnostic methods, including data mining and artificial intelligence, intending to facilitate the detection process and increase reliability. The high volume of data available in medical centers can be considered one of the main problems in using these methods. The optimal selection of essential and influential features reduces the maximum dimension for better diagnosis with more reliability of results. In this paper, a new approach uses a Binary Exchange Market Algorithm (BEMA) to identify essential and practical features in the diabetes dataset and determine the best algorithm binary function (type of sigmoid function) to improve the performance of the EMA algorithm is presented. For validation and efficiency of the proposed BEMA algorithm, several SVM, KNN, and NB classification models have been used to train and test the final model. The results obtained from the evaluations show that the proposed BEMA-SVM combined method has a better performance than the previous methods to improve accuracy and offer an effect equivalent to 98.502%. Also, to provide better results and more reliability than the proposed method, researchers can use a combination of several classes with the proposed method, which is outside the scope of this study.https://doi.org/10.1186/s40537-022-00598-zDiabetesData MiningBinary Exchange Market Algorithm (BEMA)Support Vector Machine (SVM)Feature Selection (FS) |
| spellingShingle | Faranak Rezaei Maryam Abbasitabar Shirin Mirzaei Zahra Kamari Direh Sahar Ahmadi Zahra Azizi Darya Danialy Improve data classification performance in diagnosing diabetes using the Binary Exchange Market Algorithm Journal of Big Data Diabetes Data Mining Binary Exchange Market Algorithm (BEMA) Support Vector Machine (SVM) Feature Selection (FS) |
| title | Improve data classification performance in diagnosing diabetes using the Binary Exchange Market Algorithm |
| title_full | Improve data classification performance in diagnosing diabetes using the Binary Exchange Market Algorithm |
| title_fullStr | Improve data classification performance in diagnosing diabetes using the Binary Exchange Market Algorithm |
| title_full_unstemmed | Improve data classification performance in diagnosing diabetes using the Binary Exchange Market Algorithm |
| title_short | Improve data classification performance in diagnosing diabetes using the Binary Exchange Market Algorithm |
| title_sort | improve data classification performance in diagnosing diabetes using the binary exchange market algorithm |
| topic | Diabetes Data Mining Binary Exchange Market Algorithm (BEMA) Support Vector Machine (SVM) Feature Selection (FS) |
| url | https://doi.org/10.1186/s40537-022-00598-z |
| work_keys_str_mv | AT faranakrezaei improvedataclassificationperformanceindiagnosingdiabetesusingthebinaryexchangemarketalgorithm AT maryamabbasitabar improvedataclassificationperformanceindiagnosingdiabetesusingthebinaryexchangemarketalgorithm AT shirinmirzaei improvedataclassificationperformanceindiagnosingdiabetesusingthebinaryexchangemarketalgorithm AT zahrakamaridireh improvedataclassificationperformanceindiagnosingdiabetesusingthebinaryexchangemarketalgorithm AT saharahmadi improvedataclassificationperformanceindiagnosingdiabetesusingthebinaryexchangemarketalgorithm AT zahraazizi improvedataclassificationperformanceindiagnosingdiabetesusingthebinaryexchangemarketalgorithm AT daryadanialy improvedataclassificationperformanceindiagnosingdiabetesusingthebinaryexchangemarketalgorithm |