A Data-experience intelligent model to integrate human judging behavior and statistics for predicting diabetes complications

Zhang, Lei; Liu, Mandi; Xu, Xiaoling; Yue, Qi

doi:10.1016/j.aej.2022.01.053

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…In order to prevent the least effective classifier from being ineffective due to the normalization resulting in a weight of zero, we add a balancing factor α to all of the normalized AUC to balance the gap between the weights of the individual models. This is shown in equation (3). min max min , 1...5…”

Section: Normalization Auc Plus Balancing Factor Weighted Based Ensem...mentioning

confidence: 99%

“…The causes of diabetes in China are influenced by various factors, including people's dietary habits, aging, urbanization, and family history. Diabetes can lead to complications such as diabetic retinopathy, diabetic nephropathy, stroke, heart disease, and liver disease [3]. The ability to accurately identify individuals with diabetes will therefore be of great clinical importance.…”

Section: Introductionmentioning

confidence: 99%

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Prediction for diabetes by a weighted voting classifier based on normalized AUC with balancing factors

Gao

Zheng

2023

Preprint

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Diabetes is a metabolic disease characterized by hyperglycemia caused by insulin deficiency or resistance. Diabetes can lead to various co-morbidities and is a common medical problem worldwide. The prevention and control of diabetes would benefit from accurately identifying diabetic individuals. In this study, we test the best pipeline of different machine learning (ML) models (K-Nearest Neighbor, Random Forest, Support Vector Machine, XGBoost) under different pre-processing. Also, this study investigates and proposes a weighted soft-voting classification model (NAWVE), which uses the AUC obtained from the base model on the training set and adds a balancing factor as a weight after normalization. This integrated model can well balance different performance classifiers. In the prediction of diabetes, NAWVE received the highest scores in four metrics, with accuracy, AUC, f1, and recall of 0.9606, 0.9621, 0.9472, and 0.9677, respectively. Our proposed fusion model outperformed any individual classification model and Stacking with meta-learner as logistic regression.

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Section: Normalization Auc Plus Balancing Factor Weighted Based Ensem...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction for diabetes by a weighted voting classifier based on normalized AUC with balancing factors

Gao

Zheng

2023

Preprint

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Bayesian Matrix Learning by Principle Eigenvector for Completing Missing Medical Data

2023

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Since machine learning is applied in medicine, more and more medical data for prediction has been produced by monitoring patients, such as symptoms information of diabetes. This paper establishes a frame called the Diabetes Medication Bayes Matrix (DTBM) to structure the relationship between the symptoms of diabetes and the medication regimens for machine learning. The eigenvector of the DTBM is the stable distribution of different symptoms and medication regimens. Based on the DTBM, this paper proposes a machine-learning algorithm for completing missing medical data, which provides a theoretical basis for the prediction of a Bayesian matrix with missing medical information. The experimental results show the rationality and applicability of the given algorithms.

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Nomogram-Based Chronic Kidney Disease Prediction Model for Type 1 Diabetes Mellitus Patients Using Routine Pathological Data

Chowdhury

Reaz

Ali

et al. 2022

JPM

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Type 1 diabetes mellitus (T1DM) patients are a significant threat to chronic kidney disease (CKD) development during their life. However, there is always a high chance of delay in CKD detection because CKD can be asymptomatic, and T1DM patients bypass traditional CKD tests during their routine checkups. This study aims to develop and validate a prediction model and nomogram of CKD in T1DM patients using readily available routine checkup data for early CKD detection. This research utilized 1375 T1DM patients’ sixteen years of longitudinal data from multi-center Epidemiology of Diabetes Interventions and Complications (EDIC) clinical trials conducted at 28 sites in the USA and Canada and considered 17 routinely available features. Three feature ranking algorithms, extreme gradient boosting (XGB), random forest (RF), and extremely randomized trees classifier (ERT), were applied to create three feature ranking lists, and logistic regression analyses were performed to develop CKD prediction models using these ranked feature lists to identify the best performing top-ranked features combination. Finally, the most significant features were selected to develop a multivariate logistic regression-based CKD prediction model for T1DM patients. This model was evaluated using sensitivity, specificity, accuracy, precision, and F1 score on train and test data. A nomogram of the final model was further generated for easy application in clinical practices. Hypertension, duration of diabetes, drinking habit, triglycerides, ACE inhibitors, low-density lipoprotein (LDL) cholesterol, age, and smoking habit were the top-8 features ranked by the XGB model and identified as the most important features for predicting CKD in T1DM patients. These eight features were selected to develop the final prediction model using multivariate logistic regression, which showed 90.04% and 88.59% accuracy in internal and test data validation. The proposed model showed excellent performance and can be used for CKD identification in T1DM patients during routine checkups.

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A Data-experience intelligent model to integrate human judging behavior and statistics for predicting diabetes complications

Cited by 3 publications

References 16 publications

Prediction for diabetes by a weighted voting classifier based on normalized AUC with balancing factors

Prediction for diabetes by a weighted voting classifier based on normalized AUC with balancing factors

Bayesian Matrix Learning by Principle Eigenvector for Completing Missing Medical Data

Nomogram-Based Chronic Kidney Disease Prediction Model for Type 1 Diabetes Mellitus Patients Using Routine Pathological Data

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