Prediction of Type 2 Diabetes Mellitus According to Glucose Metabolism Patterns in Pregnancy Using a Novel Machine Learning Algorithm

Houri, Ohad; Gil, Yotam; Chen, Rony; Wiznitzer, Arnon; Hochberg, Alyssa; Hadar, Eran; Berezowsky, Alexandra

doi:10.1007/s40846-022-00685-9

Cited by 4 publications

(2 citation statements)

References 27 publications

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“…The performance of the proposed models was examined by measuring the false detection rate, balanced accuracy, specificity, F1-score, recall, and precision. Houri et al [11] developed diabetes prediction models using a decision tree and XGBoost.…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel MCDM-Based Framework to Recommend Machine Learning Techniques for Diabetes Prediction

Ajay Kumar,

Kamaldeep Kaur

2023

Int. j. eng. technol. innov.

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Early detection of diabetes is crucial because of its incurable nature. Several diabetes prediction models have been developed using machine learning techniques (MLTs). The performance of MLTs varies for different accuracy measures. Thus, selecting appropriate MLTs for diabetes prediction is challenging. This paper proposes a multi-criteria decision-making (MCDM) based framework for evaluating MLTs applied to diabetes prediction. Initially, three MCDM methods—WSM, TOPSIS, and VIKOR—are used to determine the individual ranks of MLTs for diabetes prediction performance by using various comparable performance measures (PMs). Next, a fusion approach is used to determine the final rank of the MLTs. The proposed method is validated by assessing the performance of 10 MLTs on the Pima Indian diabetes dataset using eight evaluation metrics for diabetes prediction. Based on the final MCDM rankings, logistic regression is recommended for diabetes prediction modeling.

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Section: Literature Reviewmentioning

confidence: 99%

A Novel MCDM-Based Framework to Recommend Machine Learning Techniques for Diabetes Prediction

Ajay Kumar,

Kamaldeep Kaur

2023

Int. j. eng. technol. innov.

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“…In order to predict the future incidence of T2DM following pregnancy in women, an XGBoost model based on parity, age, gestational age at delivery, gravidity, glucose challenge test (GCT), oral glucose tolerance test results, OGTT, and birthweight was constructed [ 69 ]. The prediction model led to an AUC of 0.85 and an accuracy rate of 91%.…”

Section: The Application Of ML and Dl Models For The Management Predi...mentioning

confidence: 99%

Recent applications of machine learning and deep learning models in the prediction, diagnosis, and management of diabetes: a comprehensive review

Afsaneh¹,

Sharifdini²,

Ghazzaghi³

et al. 2022

Diabetol Metab Syndr

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Diabetes as a metabolic illness can be characterized by increased amounts of blood glucose. This abnormal increase can lead to critical detriment to the other organs such as the kidneys, eyes, heart, nerves, and blood vessels. Therefore, its prediction, prognosis, and management are essential to prevent harmful effects and also recommend more useful treatments. For these goals, machine learning algorithms have found considerable attention and have been developed successfully. This review surveys the recently proposed machine learning (ML) and deep learning (DL) models for the objectives mentioned earlier. The reported results disclose that the ML and DL algorithms are promising approaches for controlling blood glucose and diabetes. However, they should be improved and employed in large datasets to affirm their applicability.

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RETRACTED ARTICLE: Utilizing fog computing and explainable deep learning techniques for gestational diabetes prediction

El-Rashidy

ElSayed

El-Ghamry

et al. 2022

Neural Comput & Applic

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Gestational diabetes mellitus (GDM) is one of the pregnancy complications that poses a significant risk on mothers and babies as well. GDM usually diagnosed at 22–26 of gestation. However, the early prediction is desirable as it may contribute to decrease the risk. The continuous monitoring for mother’s vital signs helps in predicting any deterioration during pregnancy. The originality of this paper is to provide comprehensive framework for pregnancy women monitoring. The proposed Data Replacement and Prediction Framework consists of three layers which are: (i) IoT Layer, (ii) Fog Layer, and (iii) Cloud Layer. The first layer used IOT sensors to aggregate vital sings from pregnancies using invasive and noninvasive sensors. Then the vital signs transmitted to fog nodes to processed and finally stored in the cloud layer. The main contribution in this paper is located in the fog layer producing GDM module to implement two influential tasks which are: (i) Data Finding Methodology (DFM), and (ii) Explainable Prediction Algorithm (EPM) using DNN. First, the DFM is used to replace the unused data to free the cache space for the new incoming data items. The cache replacement is very important in the case of healthcare system as the incoming vital signs are frequent and must be replaced continuously. Second, the EPM is used to predict the incidence of GDM that may occur in the second trimester of the pregnancy. To evaluate our model, we extract data of 16,354 pregnancy women from medical information mart for intensive care (MIMIC III) benchmark dataset. For each woman, vital signs, demographic data and laboratory tests was aggregated. The results of the prediction model superior the state of the art (ACC = 0.957, AUC = 0.942). Regarding to explainability, we utilized Shapley additive explanation framework to provide local and global explanation for the developed models. Overall, the proposed framework is medically intuitive, allow the early prediction of GDM with cost effective solution.

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Prediction of Type 2 Diabetes Mellitus According to Glucose Metabolism Patterns in Pregnancy Using a Novel Machine Learning Algorithm

Cited by 4 publications

References 27 publications

A Novel MCDM-Based Framework to Recommend Machine Learning Techniques for Diabetes Prediction

A Novel MCDM-Based Framework to Recommend Machine Learning Techniques for Diabetes Prediction

Recent applications of machine learning and deep learning models in the prediction, diagnosis, and management of diabetes: a comprehensive review

RETRACTED ARTICLE: Utilizing fog computing and explainable deep learning techniques for gestational diabetes prediction

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