Accuracy Improvement for Diabetes Disease Classification: A Case on a Public Medical Dataset
As a chronic disease, diabetes mellitus has emerged as a worldwide epidemic. Providing diagnostic aid for diabetes disease by using a set of data that contains only medical information obtained without advanced medical equipment, can help numbers of people who want to discover the disease or the ris...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2017-09-01
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| Series: | Fuzzy Information and Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1616865817302315 |
| _version_ | 1797765897681633280 |
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| author | Mehrbakhsh Nilashi Othman Ibrahim Mohammad Dalvi Hossein Ahmadi Leila Shahmoradi |
| author_facet | Mehrbakhsh Nilashi Othman Ibrahim Mohammad Dalvi Hossein Ahmadi Leila Shahmoradi |
| author_sort | Mehrbakhsh Nilashi |
| collection | DOAJ |
| description | As a chronic disease, diabetes mellitus has emerged as a worldwide epidemic. Providing diagnostic aid for diabetes disease by using a set of data that contains only medical information obtained without advanced medical equipment, can help numbers of people who want to discover the disease or the risk of disease at an early stage. This can possibly make a huge positive impact on a lot of peoples lives. The aim of this study is to classify diabetes disease by developing an intelligence system using machine learning techniques. Our method is developed through clustering, noise removal and classification approaches. Accordingly, we use SOM, PCA and NN for clustering, noise removal and classification tasks, respectively. Experimental results on Pima Indian Diabetes dataset show that proposed method remarkably improves the accuracy of prediction in relation to methods developed in the previous studies. The hybrid intelligent system can assist medical practitioners in the healthcare practice as a decision support system. |
| first_indexed | 2024-03-12T20:16:43Z |
| format | Article |
| id | doaj.art-aaff84d1d6b54718a8a15dc97eeade90 |
| institution | Directory Open Access Journal |
| issn | 1616-8658 |
| language | English |
| last_indexed | 2024-03-12T20:16:43Z |
| publishDate | 2017-09-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Fuzzy Information and Engineering |
| spelling | doaj.art-aaff84d1d6b54718a8a15dc97eeade902023-08-02T01:16:39ZengTsinghua University PressFuzzy Information and Engineering1616-86582017-09-019334535710.1016/j.fiae.2017.09.006Accuracy Improvement for Diabetes Disease Classification: A Case on a Public Medical DatasetMehrbakhsh Nilashi0Othman Ibrahim1Mohammad Dalvi2Hossein Ahmadi3Leila Shahmoradi4Faculty of Computing, Universiti Teknologi Malaysia, 81310 Skudai, Johor, MalaysiaDepartment of Computer Engineering, Lahijan Branch, Islamic Azad University, Lahijan, IranDepartment of Mechatronics Engineering, University of Isfahan, Isfahan, IranHealth Information Management Department, 5th Floor, School of Allied Medical Sciences, Tehran University of Medical Sciences, No. 17, Farredanesh Alley, Ghods St Enghelab Ave, IranDepartment of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, IranAs a chronic disease, diabetes mellitus has emerged as a worldwide epidemic. Providing diagnostic aid for diabetes disease by using a set of data that contains only medical information obtained without advanced medical equipment, can help numbers of people who want to discover the disease or the risk of disease at an early stage. This can possibly make a huge positive impact on a lot of peoples lives. The aim of this study is to classify diabetes disease by developing an intelligence system using machine learning techniques. Our method is developed through clustering, noise removal and classification approaches. Accordingly, we use SOM, PCA and NN for clustering, noise removal and classification tasks, respectively. Experimental results on Pima Indian Diabetes dataset show that proposed method remarkably improves the accuracy of prediction in relation to methods developed in the previous studies. The hybrid intelligent system can assist medical practitioners in the healthcare practice as a decision support system.http://www.sciencedirect.com/science/article/pii/S1616865817302315Diabetes disease diagnosisClusteringPCANeural Network |
| spellingShingle | Mehrbakhsh Nilashi Othman Ibrahim Mohammad Dalvi Hossein Ahmadi Leila Shahmoradi Accuracy Improvement for Diabetes Disease Classification: A Case on a Public Medical Dataset Fuzzy Information and Engineering Diabetes disease diagnosis Clustering PCA Neural Network |
| title | Accuracy Improvement for Diabetes Disease Classification: A Case on a Public Medical Dataset |
| title_full | Accuracy Improvement for Diabetes Disease Classification: A Case on a Public Medical Dataset |
| title_fullStr | Accuracy Improvement for Diabetes Disease Classification: A Case on a Public Medical Dataset |
| title_full_unstemmed | Accuracy Improvement for Diabetes Disease Classification: A Case on a Public Medical Dataset |
| title_short | Accuracy Improvement for Diabetes Disease Classification: A Case on a Public Medical Dataset |
| title_sort | accuracy improvement for diabetes disease classification a case on a public medical dataset |
| topic | Diabetes disease diagnosis Clustering PCA Neural Network |
| url | http://www.sciencedirect.com/science/article/pii/S1616865817302315 |
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