Detection of mild cognitive impairment in type 2 diabetes mellitus based on machine learning using privileged information

Xia, Shuiwei; Peng, Bo; Hu, Xianghua; Zhou, Limin; Chen, Chunmiao; Chen, Lu; Chen, Minjiang; Pang, Chunying; Dai, Yakang; Ji, Jiansong

doi:10.1016/j.neulet.2022.136908

Cited by 5 publications

(2 citation statements)

References 32 publications

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“…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

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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.

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Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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“…Some MRI markers of cerebral small vessel disease, especially lacunar infarcts, are more common in patients with T2DM ( Geijselaers et al, 2015 ; Lawson et al, 2020 ). Some previous studies have focused on the brain functional changes of T2DM patients using resting-state functional magnetic resonance imaging and perfusion-weighted imaging ( Chen et al, 2021 ; Xia et al, 2022 ). Furthermore, artificial intelligence (AI) combined with conventional medical imaging may be useful for detecting cognitive dysfunction in patients with T2DM.…”

Section: Introductionmentioning

confidence: 99%

MRI-based machine learning model: A potential modality for predicting cognitive dysfunction in patients with type 2 diabetes mellitus

Xu¹,

Zhao²,

et al. 2022

Front. Bioeng. Biotechnol.

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Background: Type 2 diabetes mellitus (T2DM) is a crucial risk factor for cognitive impairment. Accurate assessment of patients’ cognitive function and early intervention is helpful to improve patient’s quality of life. At present, neuropsychiatric screening tests is often used to perform this task in clinical practice. However, it may have poor repeatability. Moreover, several studies revealed that machine learning (ML) models can effectively assess cognitive impairment in Alzheimer’s disease (AD) patients. We investigated whether we could develop an MRI-based ML model to evaluate the cognitive state of patients with T2DM.Objective: To propose MRI-based ML models and assess their performance to predict cognitive dysfunction in patients with type 2 diabetes mellitus (T2DM).Methods: Fluid Attenuated Inversion Recovery (FLAIR) of magnetic resonance images (MRI) were derived from 122 patients with T2DM. Cognitive function was assessed using the Chinese version of the Montréal Cognitive Assessment Scale-B (MoCA-B). Patients with T2DM were separated into the Dementia (DM) group (n = 40), MCI group (n = 52), and normal cognitive state (N) group (n = 30), according to the MoCA scores. Radiomics features were extracted from MR images with the Radcloud platform. The variance threshold, SelectKBest, and least absolute shrinkage and selection operator (LASSO) were used for the feature selection. Based on the selected features, the ML models were constructed with three classifiers, k-NearestNeighbor (KNN), Support Vector Machine (SVM), and Logistic Regression (LR), and the validation method was used to improve the effectiveness of the model. The area under the receiver operating characteristic curve (ROC) determined the appearance of the classification. The optimal classifier was determined by the principle of maximizing the Youden index.Results: 1,409 features were extracted and reduced to 13 features as the optimal discriminators to build the radiomics model. In the validation set, ROC curves revealed that the LR classifier had the best predictive performance, with an area under the curve (AUC) of 0.831 in DM, 0.883 in MIC, and 0.904 in the N group, compared with the SVM and KNN classifiers.Conclusion: MRI-based ML models have the potential to predict cognitive dysfunction in patients with T2DM. Compared with the SVM and KNN, the LR algorithm showed the best performance.

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Ethyl gallate ameliorates diabetes-induced Alzheimer's disease-like phenotype in rats via activation of α7 nicotinic receptors and mitigation of oxidative stress

Nagori,

Pradhan,

Nakhate

2024

Biochemical and Biophysical Research Communications

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Detection of mild cognitive impairment in type 2 diabetes mellitus based on machine learning using privileged information

Cited by 5 publications

References 32 publications

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

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

MRI-based machine learning model: A potential modality for predicting cognitive dysfunction in patients with type 2 diabetes mellitus

Ethyl gallate ameliorates diabetes-induced Alzheimer's disease-like phenotype in rats via activation of α7 nicotinic receptors and mitigation of oxidative stress

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