Machine Learning-Based Predictive Modeling of Diabetic Nephropathy in Type 2 Diabetes Using Integrated Biomarkers: A Single-Center Retrospective Study DOI Creative Commons
Ying Zhu, Yiyi Zhang, Miao Yang

et al.

Diabetes Metabolic Syndrome and Obesity, Journal Year: 2024, Volume and Issue: Volume 17, P. 1987 - 1997

Published: May 1, 2024

Purpose: Diabetic nephropathy (DN), a major complication of diabetes mellitus, significantly impacts global health. Identifying individuals at risk developing DN is crucial for early intervention and improving patient outcomes. This study aims to develop validate machine learning-based predictive model using integrated biomarkers. Methods: A cross-sectional analysis was conducted on baseline dataset involving 2184 participants without DN, categorized based their development over follow-up period 36 months: (n=1270) Non-DN (n=914). Various demographic clinical parameters were analyzed. The findings validated an independent comprising 468 participants, with 273 195 remaining as the period. Machine learning algorithms, alongside traditional descriptive statistics logistic regression used statistical analyses. Results: Elevated levels serum creatinine, urea, reduced eGFR, increased prevalence retinopathy peripheral neuropathy, prominently observed in those who developed DN. Validation further confirmed model's robustness consistency. SVM demonstrated superior performance training set (AUC=0.79, F1-score=0.74) testing (AUC=0.83, F1-score=0.82), outperforming other models. Significant predictors included presence diabetic retinopathy, neuropathy. Conclusion: Integrating algorithms biomarker data offers promising avenue identifying type 2 patients 36-month Keywords: nephropathy, prediction, learning, biomarkers, stratification,

Language: Английский

Explainable Machine Learning-Based Prediction Model for Diabetic Nephropathy DOI Creative Commons

Jing-Mei Yin,

Yang Li,

Jun-Tang Xue

et al.

Journal of Diabetes Research, Journal Year: 2024, Volume and Issue: 2024, P. 1 - 13

Published: Jan. 20, 2024

The aim of this study is to analyze the effect serum metabolites on diabetic nephropathy (DN) and predict prevalence DN through a machine learning approach. dataset consists 548 patients from April 2018 2019 in Second Affiliated Hospital Dalian Medical University (SAHDMU). We select optimal 38 features least absolute shrinkage selection operator (LASSO) regression model 10-fold cross-validation. compare four algorithms, including extreme gradient boosting (XGB), random forest, decision tree, logistic regression, by AUC-ROC curves, calibration curves. quantify feature importance interaction effects predictive Shapley additive explanation (SHAP) method. XGB has best performance screen for with highest AUC value 0.966. also gains more clinical net benefits than others, fitting degree better. In addition, there are significant interactions between duration diabetes. develop algorithm DN. C2, C5DC, Tyr, Ser, Met, C24, C4DC, Cys have great contribution can possibly be biomarkers

Language: Английский

Citations

11
Artificial Intelligence for Predicting and Diagnosing Complications of Diabetes DOI Creative Commons
Jingtong Huang, Andrea M. Yeung, David G. Armstrong

et al.

Journal of Diabetes Science and Technology, Journal Year: 2022, Volume and Issue: 17(1), P. 224 - 238

Published: Sept. 19, 2022

Artificial intelligence can use real-world data to create models capable of making predictions and medical diagnosis for diabetes its complications. The aim this commentary article is provide a general perspective present recent advances on how artificial be applied improve the prediction six significant complications including (1) gestational diabetes, (2) hypoglycemia in hospital, (3) diabetic retinopathy, (4) foot ulcers, (5) peripheral neuropathy, (6) nephropathy.

Language: Английский

Citations

36
Application of machine learning in predicting the risk of postpartum depression: A systematic review DOI
Minhui Zhong, Han Zhang, Chan Yu

et al.

Journal of Affective Disorders, Journal Year: 2022, Volume and Issue: 318, P. 364 - 379

Published: Aug. 31, 2022

Language: Английский

Citations

32
Baicalin ameliorates renal fibrosis by upregulating CPT1α-mediated fatty acid oxidation in diabetic kidney disease DOI
Hongtu Hu, Weiwei Li,

Yiqun Hao

et al.

Phytomedicine, Journal Year: 2023, Volume and Issue: 122, P. 155162 - 155162

Published: Oct. 22, 2023

Language: Английский

Citations

20
Machine Learning Models for Blood Glucose Level Prediction in Patients With Diabetes Mellitus: Systematic Review and Network Meta-Analysis DOI Creative Commons
Kui Liu, Linyi Li, Yifei Ma

et al.

JMIR Medical Informatics, Journal Year: 2023, Volume and Issue: 11, P. e47833 - e47833

Published: Oct. 12, 2023

Machine learning (ML) models provide more choices to patients with diabetes mellitus (DM) properly manage blood glucose (BG) levels. However, because of numerous types ML algorithms, choosing an appropriate model is vitally important.In a systematic review and network meta-analysis, this study aimed comprehensively assess the performance in predicting BG In addition, we assessed used detect predict adverse (hypoglycemia) events by calculating pooled estimates sensitivity specificity.PubMed, Embase, Web Science, Institute Electrical Electronics Engineers Explore databases were systematically searched for studies on levels or detecting using models, from inception November 2022. Studies that different DM included. no derivation metrics excluded. The Quality Assessment Diagnostic Accuracy tool was applied quality included studies. Primary outcomes relative ranking prediction horizons (PHs) specificity events.In total, 46 eligible meta-analysis. Regarding levels, means absolute root mean square error (RMSE) PH 15, 30, 45, 60 minutes 18.88 (SD 19.71), 21.40 12.56), 21.27 5.17), 30.01 7.23) mg/dL, respectively. neural (NNM) showed highest PHs. Furthermore, positive likelihood ratio negative 8.3 (95% CI 5.7-12.0) 0.31 0.22-0.44), respectively, hypoglycemia 2.4 1.6-3.7) 0.37 0.29-0.46), hypoglycemia.Statistically significant high heterogeneity detected all subgroups, sources heterogeneity. For precise RMSE increases rise PH, NNM shows among models. Meanwhile, current have sufficient ability events, while their needs be enhanced.PROSPERO CRD42022375250; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=375250.

Language: Английский

Citations

17
Artificial intelligence and machine learning trends in kidney care DOI

Yuh‐Shan Ho,

Tibor Fülöp, Pajaree Krisanapan

et al.

The American Journal of the Medical Sciences, Journal Year: 2024, Volume and Issue: 367(5), P. 281 - 295

Published: Jan. 26, 2024

Language: Английский

Citations

8
Predictive model and risk analysis for peripheral vascular disease in type 2 diabetes mellitus patients using machine learning and shapley additive explanation DOI Creative Commons
Lianhua Liu, Bo Bi, Li Juan Cao

et al.

Frontiers in Endocrinology, Journal Year: 2024, Volume and Issue: 15

Published: Feb. 28, 2024

Background Peripheral vascular disease (PVD) is a common complication in patients with type 2 diabetes mellitus (T2DM). Early detection or prediction the risk of developing PVD important for clinical decision-making. Purpose This study aims to establish and validate models perform factor analysis T2DM using machine learning Shapley Additive Explanation(SHAP) based on electronic health records. Methods We retrospectively analyzed data from 4,372 inpatients hospital between January 1, 2021, March 28, 2023. The comprised demographic characteristics, discharge diagnoses biochemical index test results. After preprocessing feature selection Recursive Feature Elimination(RFE), dataset was split into training testing sets at ratio 8:2, Synthetic Minority Over-sampling Technique(SMOTE) employed balance set. Six learning(ML) algorithms, including decision tree (DT), logistic regression (LR), random forest (RF), support vector machine(SVM),extreme gradient boosting (XGBoost) Adaptive Boosting(AdaBoost) were applied construct models. A grid search 10-fold cross-validation conducted optimize hyperparameters. Metrics such as accuracy, precision, recall, F1-score, G-mean, area under receiver operating characteristic curve (AUC) assessed models’ effectiveness. SHAP method interpreted best-performing model. Results RFE identified optimal 12 predictors. XGBoost model outperformed other five ML models, an AUC 0.945, G-mean 0.843, accuracy 0.890, precision 0.930, recall 0.927, F1-score 0.928. importance results indicated that Hemoglobin (Hb), age, total bile acids (TBA) lipoprotein(a)(LP-a) are top four factors T2DM. Conclusion approach successfully developed good performance. associated offered physicians intuitive understanding impact key features

Language: Английский

Citations

7
Machine Learning Models for Prediction of Diabetic Microvascular Complications DOI
Sarah Kanbour, Catharine Harris, Benjamin Lalani

et al.

Journal of Diabetes Science and Technology, Journal Year: 2024, Volume and Issue: 18(2), P. 273 - 286

Published: Jan. 8, 2024

Importance and Aims: Diabetic microvascular complications significantly impact morbidity mortality. This review focuses on machine learning/artificial intelligence (ML/AI) in predicting diabetic retinopathy (DR), kidney disease (DKD), neuropathy (DN). Methods: A comprehensive PubMed search from 1990 to 2023 identified studies ML/AI models for complications. The analyzed study design, cohorts, predictors, ML techniques, prediction horizon, performance metrics. Results: Among the 74 studies, 256 featured internally validated 124 had externally models, with about half being retrospective. Since 2010, there has been a rise use of complications, mainly driven by DKD research across 27 countries. more modest increase DR DN was observed, publications fewer For all predictive achieved mean (standard deviation) c-statistic 0.79 (0.09) internal validation 0.72 (0.12) external validation. highest discrimination, c-statistics 0.81 0.74 (0.13) validation, respectively. Few DN. outcome definitions, number type technique influenced model performance. Conclusions Relevance: There is growing global interest using Research most advanced terms publication volume overall Both require research. External adherence recommended guidelines are crucial.

Language: Английский

Citations

6
Gender Differences in the Incidence of Nephropathy and Changes in Renal Function in Patients with Type 2 Diabetes Mellitus: A Retrospective Cohort Study DOI Creative Commons
Fan Zhang, Yan Han, Guojun Zheng

et al.

Diabetes Metabolic Syndrome and Obesity, Journal Year: 2024, Volume and Issue: Volume 17, P. 943 - 957

Published: Feb. 1, 2024

This research aims to examine and scrutinize gender variations in the incidence of diabetic nephropathy (DN) trajectory renal function type 2 diabetes mellitus (T2DM) patients.

Language: Английский

Citations

6
Comparison of conventional mathematical model and machine learning model based on recent advances in mathematical models for predicting diabetic kidney disease DOI Creative Commons

Yingda Sheng,

Caimei Zhang, Jing Huang

et al.

Digital Health, Journal Year: 2024, Volume and Issue: 10

Published: Jan. 1, 2024

Previous research suggests that mathematical models could serve as valuable tools for diagnosing or predicting diseases like diabetic kidney disease, which often necessitate invasive examinations conclusive diagnosis. In the big-data era, there are several modeling methods, but generally, two types recognized: conventional model and machine learning model. Each method has its advantages disadvantages, a thorough comparison of is lacking. this article, we describe briefly compare model, provide prospects in field.

Language: Английский

Citations

4