# Prediction of Diabetic Nephropathy from the Relationship between Fatigue, Sleep and Quality of Life
## 5 Discussion and Conclusions  
This is the first study to use a hybrid method, which includes traditional statistics and data-mining methods, to effectively predict the presence of diabetic nephropathy from the relationships between fatigue, sleep, and quality of life. The results show the correlation between fatigue, sleep quality, quality of life, and renal disease in diabetic patients. Using traditional statistics, we found significant correlations between related factors and diabetic nephropathy. Moreover, patients with renal disease had physical indicators (BMI, WC, HC, waist-to-hip ratio, and BP) and lifestyle factors that were significantly different from those without renal disease, and they had more severe comorbidities and were more fatigued. The results showed that when diabetic patients have poor disease control and comorbidities occur, fatigue, lethargy, and sleep disorders also appear, which confirms the conclusions in a previous study [52]. Moreover, this study also found that patients with renal syndrome had poor role physical health (PH) and physical function (PF) and lower amounts of exercise, as well as a \begin{table}
\begin{tabular}{l} \hline \hline
**Variable Names** \\ \hline PC sugar (postprandial 2 hours’ plasma glucose level), urine microalbumin, microalbumin-to-creatinine ratio, body height, body weight, waist circumference, hip circumference, diastolic blood pressure, alcohol drinking behavior, exercise. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The 10 representative variables. Figure 2: Prediction accuracy of SVM (support vector machine), PCA (principal component analysis) + SVM, GA (genetic algorithm) + SVM, and C4.5(C4.5 decision tree) + SVM. higher comorbidity index. Regression models were used to determine the indicators (fatigue scores, HbA1c, and comorbidities) that can predict diabetic nephropathy. This result is consistent with those in previous studies that concluded uncontrolled glycemia is highly associated with fatigue [21; 53]. In addition, we used a feature selection method to determine the best predictive model by applying only 10 representative metrics (PC sugar, urine microalbumin, M/C, BH, BW, WC, HC, DBP, alcohol drinking behavior, and exercise). In other words, with the application of these representative metrics, the SVM classifier predicted the presence of diabetic nephropathy with an accuracy as high as 74%. Furthermore, alcohol drinking behavior was not identified by traditional statistical models but was instead selected by the SVM model for predicting the presence of complications in patients with diabetic nephropathy. Therefore, the hybrid method enhances the accuracy and validity of the prediction model. On the basis of the results of this study, when a patient experiences lethargy, reduced physical strength, and insomnia, one should carefully examine whether the patient already has diabetes complications that are in the initial stage. Muscle strength and stamina training; proper nutrition, especially vitamin D and high quality protein intake; routine preventive urine screenings; and appropriate metabolic control can not only improve physical function but also slow the deterioration of the disease [54; 55]. Finally, to provide better quality of care, we suggest using a hybrid method to make a precise predication model to explore the related risk factors and the relationships among them and other diabetes complications because the selected features can help us understand the causal relationship between features and classes. Conceptualization, A.S.-Y.L. ; Data curation, J.-L.T. and W.-C.L. ; Funding acquisition, A.S.-Y.L. and W.-C.L. ; Investigation, Y.-D.J. and J.-S.H. ; Methodology, A.S.-Y.L. Y.-D.J. and W.-C.L. ; Project administration, A.S.-Y.L. ; Validation, A.S.-Y.L., Y.-D.J. and W.-C.L. ; Writing--original draft, A.S.-Y.L. and W.-C.L. ; Writing--review & editing A.S.-Y.L. and W.-C.L. All authors have read and agreed to the published version of the manuscript.