A machine learning-based diagnosis modelling of type 2 diabetes mellitus with environmental metal exposure

Zhao, Min; Jin, Wei; Qin, Wenzhi; Huang, Xin; Chen, Guangdi; Zhao, Xinyuan

doi:10.1016/j.cmpb.2023.107537

Cited by 13 publications

(3 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Data for this study were obtained from the NHANES dataset. As described by Zhao M et al, NHANES (2013–2016) is a cross-sectional survey program using complex random probability samples with a multi-stage stratified design conducted by the National Center for Health Statistics of the Centers for Disease Control and Prevention ( 15 ). The survey covers demographics, lifestyle, anthropometric, laboratory analysis, questionnaire interviews, and dietary data.…”

Section: Methodsmentioning

confidence: 99%

Creating machine learning models that interpretably link systemic inflammatory index, sex steroid hormones, and dietary antioxidants to identify gout using the SHAP (SHapley Additive exPlanations) method

Cao,

2024

Front. Immunol.

View full text Add to dashboard Cite

BackgroundThe relationship between systemic inflammatory index (SII), sex steroid hormones, dietary antioxidants (DA), and gout has not been determined. We aim to develop a reliable and interpretable machine learning (ML) model that links SII, sex steroid hormones, and DA to gout identification.MethodsThe dataset we used to study the relationship between SII, sex steroid hormones, DA, and gout was from the National Health and Nutrition Examination Survey (NHANES). Six ML models were developed to identify gout by SII, sex steroid hormones, and DA. The seven performance discriminative features of each model were summarized, and the eXtreme Gradient Boosting (XGBoost) model with the best overall performance was selected to identify gout. We used the SHapley Additive exPlanation (SHAP) method to explain the XGBoost model and its decision-making process.ResultsAn initial survey of 20,146 participants resulted in 8,550 being included in the study. Selecting the best performing XGBoost model associated with SII, sex steroid hormones, and DA to identify gout (male: AUC: 0.795, 95% CI: 0.746- 0.843, accuracy: 98.7%; female: AUC: 0.822, 95% CI: 0.754- 0.883, accuracy: 99.2%). In the male group, The SHAP values showed that the lower feature values of lutein + zeaxanthin (LZ), vitamin C (VitC), lycopene, zinc, total testosterone (TT), vitamin E (VitE), and vitamin A (VitA), the greater the positive effect on the model output. In the female group, SHAP values showed that lower feature values of E2, zinc, lycopene, LZ, TT, and selenium had a greater positive effect on model output.ConclusionThe interpretable XGBoost model demonstrated accuracy, efficiency, and robustness in identifying associations between SII, sex steroid hormones, DA, and gout in participants. Decreased TT in males and decreased E2 in females may be associated with gout, and increased DA intake and decreased SII may reduce the potential risk of gout.

show abstract

Section: Methodsmentioning

confidence: 99%

Creating machine learning models that interpretably link systemic inflammatory index, sex steroid hormones, and dietary antioxidants to identify gout using the SHAP (SHapley Additive exPlanations) method

Cao,

2024

Front. Immunol.

View full text Add to dashboard Cite

show abstract

“…Currently, machine learning models such as the random forest (RF) ( 8 – 10 ), support vector machine (SVM) ( 8 , 11 ), logistic regression (LR) ( 11 – 13 ), and eXtreme gradient boosting (XGBoost) ( 9 , 14 ) have been developed for constructing accurate system of T2DM prediction. Some studies have also employed machine learning techniques to identify indicators associated with T2DM, such as the white blood cell (WBC) ( 15 ), urinary and dietary metal exposure ( 16 ) and serum calcium ( 17 ). These works demonstrate the effectiveness of machine learning in predicting T2DM and identifying relevant indicator information.…”

Section: Introductionmentioning

confidence: 99%

Identifying diagnostic indicators for type 2 diabetes mellitus from physical examination using interpretable machine learning approach

Lv,

Luo,

Huang

et al. 2024

Front. Endocrinol.

View full text Add to dashboard Cite

BackgroundIdentification of patients at risk for type 2 diabetes mellitus (T2DM) can not only prevent complications and reduce suffering but also ease the health care burden. While routine physical examination can provide useful information for diagnosis, manual exploration of routine physical examination records is not feasible due to the high prevalence of T2DM.ObjectivesWe aim to build interpretable machine learning models for T2DM diagnosis and uncover important diagnostic indicators from physical examination, including age- and sex-related indicators.MethodsIn this study, we present three weighted diversity density (WDD)-based algorithms for T2DM screening that use physical examination indicators, the algorithms are highly transparent and interpretable, two of which are missing value tolerant algorithms.PatientsRegarding the dataset, we collected 43 physical examination indicator data from 11,071 cases of T2DM patients and 126,622 healthy controls at the Affiliated Hospital of Southwest Medical University. After data processing, we used a data matrix containing 16004 EHRs and 43 clinical indicators for modelling.ResultsThe indicators were ranked according to their model weights, and the top 25% of indicators were found to be directly or indirectly related to T2DM. We further investigated the clinical characteristics of different age and sex groups, and found that the algorithms can detect relevant indicators specific to these groups. The algorithms performed well in T2DM screening, with the highest area under the receiver operating characteristic curve (AUC) reaching 0.9185.ConclusionThis work utilized the interpretable WDD-based algorithms to construct T2DM diagnostic models based on physical examination indicators. By modeling data grouped by age and sex, we identified several predictive markers related to age and sex, uncovering characteristic differences among various groups of T2DM patients.

show abstract

“…Machine learning (ML) methods have been used to solve several problems recently, such as diagnosing cancer [9], COVID-19 [10], autism [11,12], meningitis, diabetes, and heart disease. Recent research suggests that ML can summarize patient characteristics and predict T2DM risk [13][14][15][16][17]. The authors of Haque and Alharbi [18] investigated 18 features of T2DM in Bangladesh.…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Machine Learning Techniques for the Detection of Type-2 Diabetes Mellitus: Experiences from Bangladesh

et al. 2023

View full text Add to dashboard Cite

Diabetes is a chronic disease caused by a persistently high blood sugar level, causing other chronic diseases, including cardiovascular, kidney, eye, and nerve damage. Prompt detection plays a vital role in reducing the risk and severity associated with diabetes, and identifying key risk factors can help individuals become more mindful of their lifestyles. In this study, we conducted a questionnaire-based survey utilizing standard diabetes risk variables to examine the prevalence of diabetes in Bangladesh. To enable prompt detection of diabetes, we compared different machine learning techniques and proposed an ensemble-based machine learning framework that incorporated algorithms such as decision tree, random forest, and extreme gradient boost algorithms. In order to address class imbalance within the dataset, we initially applied the synthetic minority oversampling technique (SMOTE) and random oversampling (ROS) techniques. We evaluated the performance of various classifiers, including decision tree (DT), logistic regression (LR), support vector machine (SVM), gradient boost (GB), extreme gradient boost (XGBoost), random forest (RF), and ensemble technique (ET), on our diabetes datasets. Our experimental results showed that the ET outperformed other classifiers; to further enhance its effectiveness, we fine-tuned and evaluated the hyperparameters of the ET. Using statistical and machine learning techniques, we also ranked features and identified that age, extreme thirst, and diabetes in the family are significant features that prove instrumental in the detection of diabetes patients. This method has great potential for clinicians to effectively identify individuals at risk of diabetes, facilitating timely intervention and care.

show abstract

A machine learning-based diagnosis modelling of type 2 diabetes mellitus with environmental metal exposure

Cited by 13 publications

References 39 publications

Creating machine learning models that interpretably link systemic inflammatory index, sex steroid hormones, and dietary antioxidants to identify gout using the SHAP (SHapley Additive exPlanations) method

Creating machine learning models that interpretably link systemic inflammatory index, sex steroid hormones, and dietary antioxidants to identify gout using the SHAP (SHapley Additive exPlanations) method

Identifying diagnostic indicators for type 2 diabetes mellitus from physical examination using interpretable machine learning approach

A Comparison of Machine Learning Techniques for the Detection of Type-2 Diabetes Mellitus: Experiences from Bangladesh

Contact Info

Product

Resources

About