Distal Symmetric Polyneuropathy Identification in Type 2 Diabetes Subjects: A Random Forest Approach

Maeda-Gutiérrez, Valeria; Galván-Tejada, Carlos E.; Cruz, Miguel; Valladares‐Salgado, Adán; Galván-Tejada, Jorge I.; Gamboa-Rosales, Hamurabi; García-Hernández, Alejandra; Luna-García, Huizilopoztli; González-Curiel, Irma; Martínez-Acuña, Mónica

doi:10.3390/healthcare9020138

Cited by 12 publications

(9 citation statements)

References 57 publications

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“…The performance of our model was found to be consistent with other prediction models developed to predict diabetic neuropathy among DM patients, using hypertension, age, heart rate and BMI as predictors, AUC; 71% in china(69) using hypertension, comorbidities, gender, age, obesity, abnormal triglycerides as predictors, AUC; 75% in china (70),but better than a study using glomerular filtration rate, glibenclamide and creatinine as predictors AUC=66.05% in Mexico (71), using age, FBG, PBG, HbA1c, LDL, HDL and BMI as predictors AUC; 55.6% in china(72), using FBG, BMI, age as predictors AUC; 63.50 in Korea (73). This might be due to difference in study participants involved through socio-demographic characteristics, difference number of predictors used in the model development (74).…”

Section: Discussionsupporting

confidence: 82%

Prediction of the risk for diabetic neuropathy among diabetes mellitus patients at selected referral hospitals, in Amhara regional state Northwest Ethiopia, January, 2005- December 2021; Development and validation of risk prediction model

Gelaw¹,

Muche

Alem

et al. 2022

Preprint

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Background:-Diabetic neuropathy is the most common complication in both Type-1 and Type-2 DM patients with more than one half of all patients developing nerve dysfunction in their lifetime. Although, risk prediction model was developed for diabetic neuropathy in developed countries, It is not applicable in clinical practice, due to poor data, methodological problems, inappropriately analyzed and reported. To date, no risk prediction model developed for diabetic neuropathy among DM in Ethiopia, Therefore, this study aimed prediction the risk of diabetic neuropathy among DM patients, used for guiding in clinical decision making for clinicians Objective:- Prediction of the risk for diabetic neuropathy among diabetes mellitus patients, in selected referral hospitals, Amhara regional state, northwest Ethiopia,2022 Methods:- A retrospective follow up study was conducted with a total of 808 DM patients were enrolled from January 1,2005 to December 30,2021 at two selected referral hospitals in Amhara regional state. Multi-stage sampling techniques were used and the data was collected by checklist from medical records by Kobo collect and exported to STATA version-17 for analysis. Lasso method were used to select predictors and entered to multivariable logistic regression with P-value<0.05 was used for nomogram development. Model performance was assessed by AUC and calibration plot. Internal validation was done through bootstrapping method and decision curve analysis was performed to evaluate net benefit of model Results:-The incidence proportion of diabetic neuropathy among DM patients was 21.29 %( 95% CI; 18.59, 24.25).In multivariable logistic regression glycemic control, other comorbidities, physical activity, hypertension, alcohol drinking, type of treatment , white blood cells and red blood cells count were statistically significant. Nomogram was developed, has discriminating power AUC; 73.2 %( 95% CI; 69.0%, 77.3%) and calibration test (P-value = 0.45). It was internally validated by bootstrapping method with discrimination performance 71.7(95% CI; 67.2 %, 75.9%).It had less optimism coefficient (0.015).To make nomogram accessible, mobile based tool were developed. In machine learning, classification and regression tree has discriminating performance of 70.2 %( 95 % CI; 65.8%, 74.6%).The model had high net benefit at different threshold probabilities in both nomogram and classification and regression tree. Conclusion:- The developed nomogram and decision tree, has good level of accuracy and well calibration, easily individualized prediction of diabetic neuropathy. Both models had added net benefit in clinical practice and to be clinically applicable mobile based tool were developed.

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Section: Discussionsupporting

confidence: 82%

Prediction of the risk for diabetic neuropathy among diabetes mellitus patients at selected referral hospitals, in Amhara regional state Northwest Ethiopia, January, 2005- December 2021; Development and validation of risk prediction model

Gelaw¹,

Muche

Alem

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Through these preprocesses, we are confident that we have increased the reliability of laboratory data and created a more accurate predictive model. When compared to previous studies that made predictive models of DSPN using ML algorithms in diabetic patients ( Table 7 ), they did not explain what time point was used or whether there was any consideration of the amount of change in the laboratory data in addition to the data imputation process that handles missing data [ 24 , 25 , 27 , 38 ]. In addition, they did not provide any diagnostic tools, such as decision tree or nomogram, except Dagliati et al [ 25 ].…”

Section: Discussionmentioning

confidence: 91%

Prediction of Diabetic Sensorimotor Polyneuropathy Using Machine Learning Techniques

Shin

Lee

Yoo

et al. 2021

JCM

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Diabetic sensorimotor polyneuropathy (DSPN) is a major complication in patients with diabetes mellitus (DM), and early detection or prediction of DSPN is important for preventing or managing neuropathic pain and foot ulcer. Our aim is to delineate whether machine learning techniques are more useful than traditional statistical methods for predicting DSPN in DM patients. Four hundred seventy DM patients were classified into four groups (normal, possible, probable, and confirmed) based on clinical and electrophysiological findings of suspected DSPN. Three ML methods, XGBoost (XGB), support vector machine (SVM), and random forest (RF), and their combinations were used for analysis. RF showed the best area under the receiver operator characteristic curve (AUC, 0.8250) for differentiating between two categories—criteria by clinical findings (normal, possible, and probable groups) and those by electrophysiological findings (confirmed group)—and the result was superior to that of linear regression analysis (AUC = 0.6620). Average values of serum glucose, International Federation of Clinical Chemistry (IFCC), HbA1c, and albumin levels were identified as the four most important predictors of DSPN. In conclusion, machine learning techniques, especially RF, can predict DSPN in DM patients effectively, and electrophysiological analysis is important for identifying DSPN.

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“…It also performs well on large datasets and can sort the features by importance. The advantage of using RF is that the algorithm provides higher accuracy compared to a single decision tree, it can handle datasets with a large number of predictive variables, and it can be used for variable selection [ 54 ].…”

Section: Methodsmentioning

confidence: 99%

Applying an Improved Stacking Ensemble Model to Predict the Mortality of ICU Patients with Heart Failure

Chiu

Chien

et al. 2022

JCM

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Cardiovascular diseases have been identified as one of the top three causes of death worldwide, with onset and deaths mostly due to heart failure (HF). In ICU, where patients with HF are at increased risk of death and consume significant medical resources, early and accurate prediction of the time of death for patients at high risk of death would enable them to receive appropriate and timely medical care. The data for this study were obtained from the MIMIC-III database, where we collected vital signs and tests for 6699 HF patient during the first 24 h of their first ICU admission. In order to predict the mortality of HF patients in ICUs more precisely, an integrated stacking model is proposed and applied in this paper. In the first stage of dataset classification, the datasets were subjected to first-level classifiers using RF, SVC, KNN, LGBM, Bagging, and Adaboost. Then, the fusion of these six classifier decisions was used to construct and optimize the stacked set of second-level classifiers. The results indicate that our model obtained an accuracy of 95.25% and AUROC of 82.55% in predicting the mortality rate of HF patients, which demonstrates the outstanding capability and efficiency of our method. In addition, the results of this study also revealed that platelets, glucose, and blood urea nitrogen were the clinical features that had the greatest impact on model prediction. The results of this analysis not only improve the understanding of patients’ conditions by healthcare professionals but allow for a more optimal use of healthcare resources.

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Distal Symmetric Polyneuropathy Identification in Type 2 Diabetes Subjects: A Random Forest Approach

Cited by 12 publications

References 57 publications

Prediction of the risk for diabetic neuropathy among diabetes mellitus patients at selected referral hospitals, in Amhara regional state Northwest Ethiopia, January, 2005- December 2021; Development and validation of risk prediction model

Prediction of the risk for diabetic neuropathy among diabetes mellitus patients at selected referral hospitals, in Amhara regional state Northwest Ethiopia, January, 2005- December 2021; Development and validation of risk prediction model

Prediction of Diabetic Sensorimotor Polyneuropathy Using Machine Learning Techniques

Applying an Improved Stacking Ensemble Model to Predict the Mortality of ICU Patients with Heart Failure

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