Improving Neural Network Performance with Feature Selection Using Pearson Correlation Method for Diabetes Disease Detection
Diabetic or silent killer diseases are an alarming scourge for the world and are classed as serious diseases. In Indonesia, the increase in diabetics occurred by 2% in vulnerable times between 2013 to 2018. This affects all sectors, both medical services and the financial sector. The Neural Network...
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| Format: | Article |
| Language: | Indonesian |
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Universitas Muhammadiyah Purwokerto
2021-05-01
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| Series: | Jurnal Informatika |
| Subjects: | |
| Online Access: | http://jurnalnasional.ump.ac.id/index.php/JUITA/article/view/9941 |
| _version_ | 1819135982479343616 |
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| author | April Firman Daru Mohammad Burhan Hanif Edi Widodo |
| author_facet | April Firman Daru Mohammad Burhan Hanif Edi Widodo |
| author_sort | April Firman Daru |
| collection | DOAJ |
| description | Diabetic or silent killer diseases are an alarming scourge for the world and are classed as serious diseases. In Indonesia, the increase in diabetics occurred by 2% in vulnerable times between 2013 to 2018. This affects all sectors, both medical services and the financial sector. The Neural Network method as a data mining algorithm is present to overcome the burden that arises as an early detection analysis of the onset of disease. However, Neural Network has slow training capabilities and can identify important attributes in the data resulting in a decrease in performance. Pearson correlation is good at handling data with mixed-type attributes and is good at measuring information between attributes and attributes with labels. With this, the purpose of this study will be to use the Pearson correlation method as a selection of features to improve neural network performance in diabetes detection and measure the extent of accuracy obtained from the method. The dataset used is diabetes data 130-US hospital UCI with a record number of 101767 and the number of attributes as many as 50 attributes. The results of this study found that Pearson correlation can improve neural network accuracy performance from 94.93% to 96.00%. As for the evaluation results on the AUC value increased from 0.8077 to 0.8246. Thus Pearson's Correlation algorithm can work well for feature selection on neural network methods and can provide solutions to improved diabetes detection accuracy. |
| first_indexed | 2024-12-22T10:27:44Z |
| format | Article |
| id | doaj.art-c0651434fb8e45968364b70a1f291b4f |
| institution | Directory Open Access Journal |
| issn | 2086-9398 2579-8901 |
| language | Indonesian |
| last_indexed | 2024-12-22T10:27:44Z |
| publishDate | 2021-05-01 |
| publisher | Universitas Muhammadiyah Purwokerto |
| record_format | Article |
| series | Jurnal Informatika |
| spelling | doaj.art-c0651434fb8e45968364b70a1f291b4f2022-12-21T18:29:25ZindUniversitas Muhammadiyah PurwokertoJurnal Informatika2086-93982579-89012021-05-019112313010.30595/juita.v9i1.99413744Improving Neural Network Performance with Feature Selection Using Pearson Correlation Method for Diabetes Disease DetectionApril Firman Daru0Mohammad Burhan Hanif1Edi WidodoUniversitas SemarangUniversitas SemarangDiabetic or silent killer diseases are an alarming scourge for the world and are classed as serious diseases. In Indonesia, the increase in diabetics occurred by 2% in vulnerable times between 2013 to 2018. This affects all sectors, both medical services and the financial sector. The Neural Network method as a data mining algorithm is present to overcome the burden that arises as an early detection analysis of the onset of disease. However, Neural Network has slow training capabilities and can identify important attributes in the data resulting in a decrease in performance. Pearson correlation is good at handling data with mixed-type attributes and is good at measuring information between attributes and attributes with labels. With this, the purpose of this study will be to use the Pearson correlation method as a selection of features to improve neural network performance in diabetes detection and measure the extent of accuracy obtained from the method. The dataset used is diabetes data 130-US hospital UCI with a record number of 101767 and the number of attributes as many as 50 attributes. The results of this study found that Pearson correlation can improve neural network accuracy performance from 94.93% to 96.00%. As for the evaluation results on the AUC value increased from 0.8077 to 0.8246. Thus Pearson's Correlation algorithm can work well for feature selection on neural network methods and can provide solutions to improved diabetes detection accuracy.http://jurnalnasional.ump.ac.id/index.php/JUITA/article/view/9941neural network, pearson correlation, diabetes. |
| spellingShingle | April Firman Daru Mohammad Burhan Hanif Edi Widodo Improving Neural Network Performance with Feature Selection Using Pearson Correlation Method for Diabetes Disease Detection Jurnal Informatika neural network, pearson correlation, diabetes. |
| title | Improving Neural Network Performance with Feature Selection Using Pearson Correlation Method for Diabetes Disease Detection |
| title_full | Improving Neural Network Performance with Feature Selection Using Pearson Correlation Method for Diabetes Disease Detection |
| title_fullStr | Improving Neural Network Performance with Feature Selection Using Pearson Correlation Method for Diabetes Disease Detection |
| title_full_unstemmed | Improving Neural Network Performance with Feature Selection Using Pearson Correlation Method for Diabetes Disease Detection |
| title_short | Improving Neural Network Performance with Feature Selection Using Pearson Correlation Method for Diabetes Disease Detection |
| title_sort | improving neural network performance with feature selection using pearson correlation method for diabetes disease detection |
| topic | neural network, pearson correlation, diabetes. |
| url | http://jurnalnasional.ump.ac.id/index.php/JUITA/article/view/9941 |
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