Advanced Techniques for Predicting the Future Progression of Type 2 Diabetes

Islam, Sirajul; Qaraqe, Marwa; Belhaouari, Samir Brahim; Abdul‐Ghani, Muhammad

doi:10.1109/access.2020.3005540

Cited by 26 publications

(20 citation statements)

References 29 publications

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“…Novel feature extraction methods are proposed to select relevant factors required for effective prediction. The most significant risk factors are extracted from the whole dataset based on the attribute scores [11,12]. There exist works that account for both risk factors and symptom-oriented variables for constructing the model [13].…”

Section: A Factors For Prediction Methodsmentioning

confidence: 99%

Average Weighted Objective Distance-Based Method for Type 2 Diabetes Prediction

2021

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Early detection of Type 2 diabetes is necessary for its prevention. The prediction models for detection systems normally employ common factors that may not properly fit all persons having different health conditions. Therefore, this study proposes a method for type 2 diabetes prediction with factors representing personal health conditions. More specifically, this study proposes a novel prediction method named Average Weighted Objective Distance (AWOD) based on the assumption that the individual has diverse health conditions resulting from different individual factors, a requirement for an effective prediction model. AWOD is a modification of Weighted Objective Distance (WOD) by applying information gain to reveal significant and insignificant individual factors having different priorities, which are represented by different weights. For AWOD, the data set is divided into a training set used to determine all relevant thresholds and constant values required for AWOD calculation and the testing set. In particular, AWOD is designed for binary classification problems with a relatively small dataset. To validate the proposed method, two datasets from open sources, Pima Indians Diabetes (Dataset 1) and Mendeley Data for Diabetes (Dataset 2) each containing 392 records, were studied. The prediction performance for both datasets is compared with the machine learning-based prediction methods, including K-Nearest Neighbors, Support Vector Machines, Random Forest, and Deep Learning. The comparison results showed that the proposed method provided 93.22% and 98.95% accuracy for Dataset 1 and Dataset 2, respectively, which are higher than those provided by other machine learning-based methods.

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Section: A Factors For Prediction Methodsmentioning

confidence: 99%

Average Weighted Objective Distance-Based Method for Type 2 Diabetes Prediction

2021

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“…Marini et al [ 9 ] developed a Dynamic Bayesian Network (DBN) model to simulate of development of several clinical complications of type 1 diabetes. Islam et al [ 10 ] applied a machine learning pipeline to predict future development of type 2 diabetes based on finding an optimal set of risk-factors. This is not the case for our purpose because we aim to use a minimally supervised approach to generate the full trajectories of chronic diseases, which does not require either a training dataset with patient disease stages labeled or domain knowledge that specifies the indicators for stage transitions.…”

Section: Introductionmentioning

confidence: 99%

Using an optimized generative model to infer the progression of complications in type 2 diabetes patients

Wang

Lin

Xiong

et al. 2022

BMC Med Inform Decis Mak

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Background People live a long time in pre-diabetes/early diabetes without a formal diagnosis or management. Heterogeneity of progression coupled with deficiencies in electronic health records related to incomplete data, discrete events, and irregular event intervals make identification of pre-diabetes and critical points of diabetes progression challenging. Methods We utilized longitudinal electronic health records of 9298 patients with type 2 diabetes or prediabetes from 2005 to 2016 from a large regional healthcare delivery network in China. We optimized a generative Markov-Bayesian-based model to generate 5000 synthetic illness trajectories. The synthetic data were manually reviewed by endocrinologists. Results We build an optimized generative progression model for type 2 diabetes using anchor information to reduce the number of parameters learning in the third layer of the model from $$O\left(N\times W\right)$$ O N × W to $$O\left((N-C)\times W\right)$$ O ( N - C ) × W , where $$N$$ N is the number of clinical findings, $$W$$ W is the number of complications, $$C$$ C is the number of anchors. Based on this model, we infer the relationships between progression stages, the onset of complication categories, and the associated diagnoses during the whole progression of type 2 diabetes using electronic health records. Discussion Our findings indicate that 55.3% of single complications and 31.8% of complication patterns could be predicted early and managed appropriately to potentially delay (as it is a progressive disease) or prevented (by lifestyle modifications that keep patient from developing/triggering diabetes in the first place). Conclusions The full type 2 diabetes patient trajectories generated by the chronic disease progression model can counter a lack of real-world evidence of desired longitudinal timeframe while facilitating population health management.

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“…13 A few available classifiers are decision tree (DT), support vector machine (SVM), logistic regression (LR), etc. [14][15][16][17][18][19][20] These classifiers are capable for handling and integrating various datasets to construct useful datasets and to diagnosis the pattern thoroughly. 21 Therefore, this study proposes an efficient ML based pattern prediction technique called diabetes pattern detection model through tree ensemble clustering classifier (DDTEC) to achieve higher classification and prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…A few available classifiers are decision tree (DT), support vector machine (SVM), logistic regression (LR), etc 14–20 . These classifiers are capable for handling and integrating various datasets to construct useful datasets and to diagnosis the pattern thoroughly 21 .…”

Section: Introductionmentioning

confidence: 99%

Machine learning based pattern detection technique for diabetes mellitus prediction

Sumathi

Meganathan

2022

Concurrency and Computation

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In recent years, a few popular classifiers and computational tools have been introduced for the prediction of diabetes mellitus. The robustness of these methodologies is to predict and classify the diabetes mellitus. Diabetes is the most fatal disease that affects the life style of humans. More researchers have focusing on diabetes issues to design efficient classifiers for better classification and prediction. This research work is analyzed with two existing classifiers: (i) hybrid prediction model for type 2 diabetes pattern (HPMT2D) and (ii) type 2 diabetes mellitus prediction model (T2DMPM). These two classifiers are implemented in R tool. An efficient classifier must needed to diagnose and treatment well in advance. Therefore, this article proposes an efficient machine learning based pattern prediction technique called diabetes pattern detection technique using tree ensemble clustering classifier (DDTEC) to accomplish this demand. The proposed classifier is implemented in R tool with BioWeka and studied thoroughly to find classification accuracy of the patterns of type 1 diabetes, type 2 diabetes, and gestational diabetes. From the experimental results, it is noticed that the existing classifiers unable to classify and predict the patterns of various diabetes mellitus with better classification accuracy and F‐measure.

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Advanced Techniques for Predicting the Future Progression of Type 2 Diabetes

Cited by 26 publications

References 29 publications

Average Weighted Objective Distance-Based Method for Type 2 Diabetes Prediction

Average Weighted Objective Distance-Based Method for Type 2 Diabetes Prediction

Using an optimized generative model to infer the progression of complications in type 2 diabetes patients

Machine learning based pattern detection technique for diabetes mellitus prediction

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