Microvascular Complications in Type-2 Diabetes: A Review of Statistical Techniques and Machine Learning Models

Objective Machine learning involves the use of algorithms without explicit instructions. Of late, machine learning models have been widely applied for the prediction of type 2 diabetes. However, no evidence synthesis of the performance of these prediction models of type 2 diabetes is available. We aim to identify machine learning prediction models for type 2 diabetes in clinical and community care settings and determine their predictive performance. Methods The systematic review of English language machine learning predictive modeling studies in 12 databases will be conducted. Studies predicting type 2 diabetes in predefined clinical or community settings are eligible. Standard CHARMS and TRIPOD guidelines will guide data extraction. Methodological quality will be assessed using a predefined risk of bias assessment tool. The extent of validation will be categorized by Reilly–Evans levels. Primary outcomes include model performance metrics of discrimination ability, calibration, and classification accuracy. Secondary outcomes include candidate predictors, algorithms used, level of validation, and intended use of models. The random-effects meta-analysis of c-indices will be performed to evaluate discrimination abilities. The c-indices will be pooled per prediction model, per model type, and per algorithm. Publication bias will be assessed through funnel plots and regression tests. Sensitivity analysis will be conducted to estimate the effects of study quality and missing data on primary outcome. The sources of heterogeneity will be assessed through meta-regression. Subgroup analyses will be performed for primary outcomes. Ethics and dissemination No ethics approval is required, as no primary or personal data are collected. Findings will be disseminated through scientific sessions and peer-reviewed journals. PROSPERO registration number CRD42019130886

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Hypoglycemia Prediction Using Machine Learning Models for Patients With Type 2 Diabetes

Journal of Diabetes Science and Technology ◽

10.1177/1932296814554260 ◽

2014 ◽

Vol 9 (1) ◽

pp. 86-90 ◽

Cited By ~ 52

Author(s):

Bharath Sudharsan ◽

Malinda Peeples ◽

Mansur Shomali

Keyword(s):

Machine Learning ◽

Type 2 Diabetes ◽

Learning Models ◽

Machine Learning Models

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Application of Machine Learning Models to Evaluate Hypoglycemia Risk in Type 2 Diabetes

Diabetes Therapy ◽

10.1007/s13300-020-00759-4 ◽

2020 ◽

Vol 11 (3) ◽

pp. 681-699 ◽

Cited By ~ 1

Author(s):

Luke Mueller ◽

Paulos Berhanu ◽

Jonathan Bouchard ◽

Veronica Alas ◽

Kenneth Elder ◽

...

Keyword(s):

Machine Learning ◽

Type 2 Diabetes ◽

Learning Models ◽

Machine Learning Models

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A Novel Intelligent System for Detection of Type 2 Diabetes with Modified Loss Function and Regularization

Proceedings of the Institute for System Programming of RAS ◽

10.15514/ispras-2021-33(2)-5 ◽

2021 ◽

Vol 33 (2) ◽

pp. 93-114

Author(s):

Mallika G.C. ◽

Abeer Alsadoon ◽

Duong Thu Hang Pham ◽

Salma Hameedi Abdullah ◽

Ha Thi Mai ◽

...

Keyword(s):

Machine Learning ◽

Type 2 Diabetes ◽

Deep Learning ◽

Intelligent System ◽

Model Solution ◽

Majority Voting ◽

Ensemble Model ◽

Pima Indians ◽

Learning Models

Type 2 Diabetes (T2DM) makes up about 90% of diabetes cases, as well as tough restriction on continuous monitoring and detecting become one of key aspects in T2DM. This research aims to develop an ensemble of several machine learning and deep learning models for early detection of T2DM with high accuracy. With high diversity of models, the ensemble will provide more excessive performance than single models. Methodology: The proposed system is modified enhanced ensemble of machine learning models for T2DM prediction. It is composed of Logistic Regression, Random Forest, SVM and Deep Neural Network models to generate a modified ensemble model. Results: The output of each model in the modified ensemble is used to figure out the final output of the system. The datasets being used for these models include Practice Fusion HER, Pima Indians diabetic's data, UCI AIM94 Dataset and CA Diabetes Prevalence 2014. In comparison to the previous solutions, the proposed ensemble model solution exposes the effectiveness of accuracy, sensitivity, and specificity. It provides an accuracy of 87.5% from 83.51% in average, sensitivity of 35.8% from 29.59% as well as specificity of 98.9% from 96.27%. The processing time of the proposed model solution with 96.6ms is faster than the state-of-the-art with 97.5ms. Conclusion: The proposed modified enhanced system in this work improves the overall prediction capability of T2DM using an ensemble of several machine learning and deep learning models. A majority voting scheme utilizes the output from several models to make the final accurate prediction. Regularization function in this work is modified in order to include the regularization of all the models in ensemble, that helps prevent the overfitting and encourages the generalization capacity of the proposed system.

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Eye-color and Type-2 diabetes phenotype prediction from genotype data using deep learning methods

BMC Bioinformatics ◽

10.1186/s12859-021-04077-9 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Muhammad Muneeb ◽

Andreas Henschel

Keyword(s):

Machine Learning ◽

Type 2 Diabetes ◽

Learning Model ◽

Machine Learning Algorithms ◽

Statistical Techniques ◽

Human Beings ◽

Eye Color ◽

Machine Learning Model ◽

Extreme Gradient Boosting

Abstract Background Genotype–phenotype predictions are of great importance in genetics. These predictions can help to find genetic mutations causing variations in human beings. There are many approaches for finding the association which can be broadly categorized into two classes, statistical techniques, and machine learning. Statistical techniques are good for finding the actual SNPs causing variation where Machine Learning techniques are good where we just want to classify the people into different categories. In this article, we examined the Eye-color and Type-2 diabetes phenotype. The proposed technique is a hybrid approach consisting of some parts from statistical techniques and remaining from Machine learning. Results The main dataset for Eye-color phenotype consists of 806 people. 404 people have Blue-Green eyes where 402 people have Brown eyes. After preprocessing we generated 8 different datasets, containing different numbers of SNPs, using the mutation difference and thresholding at individual SNP. We calculated three types of mutation at each SNP no mutation, partial mutation, and full mutation. After that data is transformed for machine learning algorithms. We used about 9 classifiers, RandomForest, Extreme Gradient boosting, ANN, LSTM, GRU, BILSTM, 1DCNN, ensembles of ANN, and ensembles of LSTM which gave the best accuracy of 0.91, 0.9286, 0.945, 0.94, 0.94, 0.92, 0.95, and 0.96% respectively. Stacked ensembles of LSTM outperformed other algorithms for 1560 SNPs with an overall accuracy of 0.96, AUC = 0.98 for brown eyes, and AUC = 0.97 for Blue-Green eyes. The main dataset for Type-2 diabetes consists of 107 people where 30 people are classified as cases and 74 people as controls. We used different linear threshold to find the optimal number of SNPs for classification. The final model gave an accuracy of 0.97%. Conclusion Genotype–phenotype predictions are very useful especially in forensic. These predictions can help to identify SNP variant association with traits and diseases. Given more datasets, machine learning model predictions can be increased. Moreover, the non-linearity in the Machine learning model and the combination of SNPs Mutations while training the model increases the prediction. We considered binary classification problems but the proposed approach can be extended to multi-class classification.

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Eye-Color and Type-2 Diabetes Phenotype Prediction From Genotype Data Using Deep Learning Methods

10.21203/rs.3.rs-125397/v1 ◽

2020 ◽

Author(s):

Muhammad Muneeb ◽

Andreas Henschel

Keyword(s):

Machine Learning ◽

Type 2 Diabetes ◽

Learning Model ◽

Machine Learning Algorithms ◽

Statistical Techniques ◽

Human Beings ◽

Eye Color ◽

Machine Learning Model ◽

Extreme Gradient Boosting

Abstract Background: Genotype-Phenotype predictions are of great importance in genetics. These predictions can help to find genetic mutations causing variations in human beings. There are many approaches for finding the association which can be broadly categorized into two classes, statistical techniques, and machine learning. Statistical techniques are good for finding the actual SNPs causing variation where Machine Learning techniques are good where we just want to classify the people into different categories. In this article, we examined the Eye-color and Type-2 diabetes phenotype. The proposed technique is a hybrid approach consisting of some parts from statistical techniques and remaining from Machine learning. Results: The main dataset for Eye-color phenotype consists of 806 people. 404 people have Blue-Green eyes where 402 people have Brown eyes. After preprocessing we generated 8 different datasets, containing different numbers of SNPs, using the mutation difference and thresholding at individual SNP. We calculated three types of mutation at each SNP no mutation, partial mutation, and full mutation. After that data is transformed for machine learning algorithms. We used about 9 classifiers, RandomForest, Extreme Gradient boosting, ANN, LSTM, GRU, BILSTM, 1DCNN, ensembles of ANN, and ensembles of LSTM which gave the best accuracy of 0.91, 0.9286, 0.945, 0.94, 0.94, 0.92, 0.95, and 0.96 percent respectively. Stacked ensembles of LSTM outperformed other algorithms for 1560 SNPs with an overall accuracy of 0.96, AUC = 0.98 for brown eyes, and AUC = 0.97 for Blue-Green eyes. The main dataset for Type-2 diabetes consists of 107 people where 30 people are classified as cases and 74 people as controls. We used different linear threshold to find the optimal number of SNPs for classification. The final model gave an accuracy of 0.97 percent. Conclusion: Genotype-phenotype predictions are very useful especially in forensic. These predictions can help to identify SNP variant association with traits and diseases. Given more datasets, machine learning model predictions can be increased. Moreover, the non-linearity in the Machine learning model and the combination of SNPs Mutations while training the model increases the prediction. We considered binary classification problems but the proposed approach can be extended to multi-class classification.

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