Harnessing machine learning models for non-invasive pre-diabetes screening in children and adolescents

Kushwaha, Savitesh; Srivastava, R. N.; Jain, Ms. Rachita; Sagar, Vivek; Aggarwal, Arun Kumar; Bhadada, Prof. Sanjay Kumar; Khanna, Poonam

doi:10.1016/j.cmpb.2022.107180

Cited by 14 publications

(7 citation statements)

References 40 publications

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“…The ML model based on extreme gradient boosting (XGB) was selected in our study because of its generalizability, low risk of overfitting, high interpretability [25], and high scalability [34]. XGB has been confirmed to be a reliable method for recognizing patterns in other diseases such as lupus erythematosus [16], traumatic brain injury-induced coagulopathy [35], epilepsy [36], diabetes [37], Alzheimer's disease [38,39], HIV [40,41], or different types of cancer [42][43][44][45][46]. We, therefore, used the aforementioned ML technique to determine which factors were most predictive of disease severity in a closed group of patients hospitalized for COVID-19 during the first two months of the pandemic, a time when the population did not yet have herd immunity and had not yet been vaccinated.…”

Section: Discussionmentioning

confidence: 99%

Approaching Personalized Medicine: The Use of Machine Learning to Determine Predictors of Mortality in a Population with SARS-CoV-2 Infection

Queipo,

Barbado,

Torres

et al. 2024

Biomedicines

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The COVID-19 pandemic demonstrated the need to develop strategies to control a new viral infection. However, the different characteristics of the health system and population of each country and hospital would require the implementation of self-systems adapted to their characteristics. The objective of this work was to determine predictors that should identify the most severe patients with COVID-19 infection. Given the poor situation of the hospitals in the first wave, the analysis of the data from that period with an accurate and fast technique can be an important contribution. In this regard, machine learning is able to objectively analyze data in hourly sets and is used in many fields. This study included 291 patients admitted to a hospital in Spain during the first three months of the pandemic. After screening seventy-one features with machine learning methods, the variables with the greatest influence on predicting mortality in this population were lymphocyte count, urea, FiO2, potassium, and serum pH. The XGB method achieved the highest accuracy, with a precision of >95%. Our study shows that the machine learning-based system can identify patterns and, thus, create a tool to help hospitals classify patients according to their severity of illness in order to optimize admission.

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Section: Discussionmentioning

confidence: 99%

Approaching Personalized Medicine: The Use of Machine Learning to Determine Predictors of Mortality in a Population with SARS-CoV-2 Infection

Queipo,

Barbado,

Torres

et al. 2024

Biomedicines

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“…Savitesh predicted the risk of pre-diabetes in children and adolescents and found that XG Boost was the best classification model with a 10-fold cross-validation score of up to 90.13%. Savitesh integrated the XG Boost algorithm into a screening tool for completing the automatic prediction of pre-diabetes [ 37 ]. Shoukun performed miner fatigue identification based on physiological indicators from ECG and EMG and found that the XG Boost model had the best accuracy and robustness with a recognition accuracy of 89.47% and AUC of 0.90, the recognition of miner fatigue based on the XG Boost model is feasible [ 38 ].…”

Section: Discussionmentioning

confidence: 99%

Risk Prediction for the Development of Hyperuricemia: Model Development Using an Occupational Health Examination Dataset

Zheng

Wang

et al. 2023

IJERPH

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OBJECTIVE: Hyperuricemia has become the second most common metabolic disease in China after diabetes, and the disease burden is not optimistic. METHODS: We used the method of retrospective cohort studies, a baseline survey completed from January to September 2017, and a follow-up survey completed from March to September 2019. A group of 2992 steelworkers was used as the study population. Three models of Logistic regression, CNN, and XG Boost were established to predict HUA incidence in steelworkers, respectively. The predictive effects of the three models were evaluated in terms of discrimination, calibration, and clinical applicability. RESULTS: The training set results show that the accuracy of the Logistic regression, CNN, and XG Boost models was 84.4, 86.8, and 86.6, sensitivity was 68.4, 72.3, and 81.5, specificity was 82.0, 85.7, and 86.8, the area under the ROC curve was 0.734, 0.724, and 0.806, and Brier score was 0.121, 0.194, and 0.095, respectively. The XG Boost model effect evaluation index was better than the other two models, and similar results were obtained in the validation set. In terms of clinical applicability, the XG Boost model had higher clinical applicability than the Logistic regression and CNN models. CONCLUSION: The prediction effect of the XG Boost model was better than the CNN and Logistic regression models and was suitable for the prediction of HUA onset risk in steelworkers.

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“…There are different works with ML for diabetes detection that used the Pima Indian dataset, which is a dataset composed of 9 characteristics and 768 patients [14][15][16][17]. For example, in [18], they implemented a model with ML using cross-validation to predict diabetes with noninvasive instruments, and they applied six ML models with different assessment metrics. As a result, the XG boost classifier obtained the best metric with 0.9013 in cross-validation, and the model with the lowest performance was SVM with 61.17%.…”

Section: Related Workmentioning

confidence: 99%

Application of Machine Learning Models for Early Detection and Accurate Classification of Type 2 Diabetes

Iparraguirre-Villanueva,

Espinola-Linares,

Flores Castañeda

et al. 2023

Diagnostics

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Early detection of diabetes is essential to prevent serious complications in patients. The purpose of this work is to detect and classify type 2 diabetes in patients using machine learning (ML) models, and to select the most optimal model to predict the risk of diabetes. In this paper, five ML models, including K-nearest neighbor (K-NN), Bernoulli Naïve Bayes (BNB), decision tree (DT), logistic regression (LR), and support vector machine (SVM), are investigated to predict diabetic patients. A Kaggle-hosted Pima Indian dataset containing 768 patients with and without diabetes was used, including variables such as number of pregnancies the patient has had, blood glucose concentration, diastolic blood pressure, skinfold thickness, body insulin levels, body mass index (BMI), genetic background, diabetes in the family tree, age, and outcome (with/without diabetes). The results show that the K-NN and BNB models outperform the other models. The K-NN model obtained the best accuracy in detecting diabetes, with 79.6% accuracy, while the BNB model obtained 77.2% accuracy in detecting diabetes. Finally, it can be stated that the use of ML models for the early detection of diabetes is very promising.

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Harnessing machine learning models for non-invasive pre-diabetes screening in children and adolescents

Cited by 14 publications

References 40 publications

Approaching Personalized Medicine: The Use of Machine Learning to Determine Predictors of Mortality in a Population with SARS-CoV-2 Infection

Approaching Personalized Medicine: The Use of Machine Learning to Determine Predictors of Mortality in a Population with SARS-CoV-2 Infection

Risk Prediction for the Development of Hyperuricemia: Model Development Using an Occupational Health Examination Dataset

Application of Machine Learning Models for Early Detection and Accurate Classification of Type 2 Diabetes

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