Prediction of Diabetic Nephropathy from the Relationship between Fatigue, Sleep and Quality of Life
Fatigue and poor sleep quality are the most common clinical complaints of people with diabetes mellitus (DM). These complaints are early signs of DM and are closely related to diabetic control and the presence of complications, which lead to a decline in the quality of life. Therefore, an accurate m...
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MDPI AG
2020-05-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/9/3282 |
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| author | Angela Shin-Yu Lien Yi-Der Jiang Jia-Ling Tsai Jawl-Shan Hwang Wei-Chao Lin |
| author_facet | Angela Shin-Yu Lien Yi-Der Jiang Jia-Ling Tsai Jawl-Shan Hwang Wei-Chao Lin |
| author_sort | Angela Shin-Yu Lien |
| collection | DOAJ |
| description | Fatigue and poor sleep quality are the most common clinical complaints of people with diabetes mellitus (DM). These complaints are early signs of DM and are closely related to diabetic control and the presence of complications, which lead to a decline in the quality of life. Therefore, an accurate measurement of the relationship between fatigue, sleep status, and the complication of DM nephropathy could lead to a specific definition of fatigue and an appropriate medical treatment. This study recruited 307 people with Type 2 diabetes from two medical centers in Northern Taiwan through a questionnaire survey and a retrospective investigation of medical records. In an attempt to identify the related factors and accurately predict diabetic nephropathy, we applied hybrid research methods, integrated biostatistics, and feature selection methods in data mining and machine learning to compare and verify the results. Consequently, the results demonstrated that patients with diabetic nephropathy have a higher fatigue level and Charlson comorbidity index (CCI) score than without neuropathy, the presence of neuropathy leads to poor sleep quality, lower quality of life, and poor metabolism. Furthermore, by considering feature selection in selecting representative features or variables, we achieved consistence results with a support vector machine (SVM) classifier and merely ten representative factors and a prediction accuracy as high as 74% in predicting the presence of diabetic nephropathy. |
| first_indexed | 2024-03-10T19:57:59Z |
| format | Article |
| id | doaj.art-f5b3e249137e42d591ccdc2678e5384e |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T19:57:59Z |
| publishDate | 2020-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-f5b3e249137e42d591ccdc2678e5384e2023-11-19T23:49:03ZengMDPI AGApplied Sciences2076-34172020-05-01109328210.3390/app10093282Prediction of Diabetic Nephropathy from the Relationship between Fatigue, Sleep and Quality of LifeAngela Shin-Yu Lien0Yi-Der Jiang1Jia-Ling Tsai2Jawl-Shan Hwang3Wei-Chao Lin4School of Nursing, College of Medicine, Chang Gung University, Taoyuan 333, TaiwanDepartment of Internal Medicine, National Taiwan University Hospital, Taipei 100, TaiwanSchool of Nursing, College of Medicine, Chang Gung University, Taoyuan 333, TaiwanDivision of Endocrinology and Metabolism, Department of Internal Medicine, Chang Gung Memorial Hospital, Linkou 333, TaiwanHealthy Aging Research Center, Chang Gung University, Taoyuan 333, TaiwanFatigue and poor sleep quality are the most common clinical complaints of people with diabetes mellitus (DM). These complaints are early signs of DM and are closely related to diabetic control and the presence of complications, which lead to a decline in the quality of life. Therefore, an accurate measurement of the relationship between fatigue, sleep status, and the complication of DM nephropathy could lead to a specific definition of fatigue and an appropriate medical treatment. This study recruited 307 people with Type 2 diabetes from two medical centers in Northern Taiwan through a questionnaire survey and a retrospective investigation of medical records. In an attempt to identify the related factors and accurately predict diabetic nephropathy, we applied hybrid research methods, integrated biostatistics, and feature selection methods in data mining and machine learning to compare and verify the results. Consequently, the results demonstrated that patients with diabetic nephropathy have a higher fatigue level and Charlson comorbidity index (CCI) score than without neuropathy, the presence of neuropathy leads to poor sleep quality, lower quality of life, and poor metabolism. Furthermore, by considering feature selection in selecting representative features or variables, we achieved consistence results with a support vector machine (SVM) classifier and merely ten representative factors and a prediction accuracy as high as 74% in predicting the presence of diabetic nephropathy.https://www.mdpi.com/2076-3417/10/9/3282fatiguesleep qualityquality of lifediabetic nephropathyfeature selectiondata mining |
| spellingShingle | Angela Shin-Yu Lien Yi-Der Jiang Jia-Ling Tsai Jawl-Shan Hwang Wei-Chao Lin Prediction of Diabetic Nephropathy from the Relationship between Fatigue, Sleep and Quality of Life Applied Sciences fatigue sleep quality quality of life diabetic nephropathy feature selection data mining |
| title | Prediction of Diabetic Nephropathy from the Relationship between Fatigue, Sleep and Quality of Life |
| title_full | Prediction of Diabetic Nephropathy from the Relationship between Fatigue, Sleep and Quality of Life |
| title_fullStr | Prediction of Diabetic Nephropathy from the Relationship between Fatigue, Sleep and Quality of Life |
| title_full_unstemmed | Prediction of Diabetic Nephropathy from the Relationship between Fatigue, Sleep and Quality of Life |
| title_short | Prediction of Diabetic Nephropathy from the Relationship between Fatigue, Sleep and Quality of Life |
| title_sort | prediction of diabetic nephropathy from the relationship between fatigue sleep and quality of life |
| topic | fatigue sleep quality quality of life diabetic nephropathy feature selection data mining |
| url | https://www.mdpi.com/2076-3417/10/9/3282 |
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