Machine learning based pattern detection technique for diabetes mellitus prediction

Sumathi, A.; Meganathan, S.

doi:10.1002/cpe.6751

Cited by 3 publications

(1 citation statement)

References 28 publications

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“…Diabetes is a metabolic disorder involving inadequate insulin production or impaired function that causes changes in blood glucose levels (BGLs) [ 1 ]. The main types include Type 1 Diabetes (T1D), Type 2 Diabetes (T2D), and gestational diabetes [ 2 , 3 ]. T1D stems from an autoimmune response that damages pancreatic β-cells [ 4 ], whereas T2D results from reduced insulin sensitivity or insufficient secretion [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

AWD-stacking: An enhanced ensemble learning model for predicting glucose levels

Yang,

Chen,

Huang

et al. 2024

PLoS ONE

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Accurate prediction of blood glucose levels is essential for type 1 diabetes optimizing insulin therapy and minimizing complications in patients with type 1 diabetes. Using ensemble learning algorithms is a promising approach. In this regard, this study proposes an improved stacking ensemble learning algorithm for predicting blood glucose level, in which three improved long short-term memory network models are used as the base model, and an improved nearest neighbor propagation clustering algorithm is adaptively weighted to this ensemble model. The OhioT1DM dataset is used to train and evaluate the performance of the proposed model. This study evaluated the performance of the proposed model using the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Matthews Correlation Coefficient (MCC) as the evaluation metrics. The experimental results demonstrate that the proposed model achieves an RMSE of 1.425 mg/dL, MAE of 0.721 mg/dL, and MCC of 0.982 mg/dL for a 30-minute prediction horizon(PH), RMSE of 3.212 mg/dL, MAE of 1.605 mg/dL, and MCC of 0.950 mg/dL for a 45-minute PH; and RMSE of 6.346 mg/dL, MAE of 3.232 mg/dL, and MCC of 0.930 mg/dL for a 60-minute PH. Compared with the best non-ensemble model StackLSTM, the RMSE and MAE were improved by up to 27.92% and 65.32%, respectively. Clarke Error Grid Analysis and critical difference diagram revealed that the model errors were within 10%. The model proposed in this study exhibits state-of-the-art predictive performance, making it suitable for clinical decision-making and of significant importance for the effective treatment of diabetes in patients.

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Section: Introductionmentioning

confidence: 99%

AWD-stacking: An enhanced ensemble learning model for predicting glucose levels

Yang,

Chen,

Huang

et al. 2024

PLoS ONE

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A Diabetes Risk Predicting Method With Multi-Strategy Counterfactual-Based Data Augmentation

Chen¹,

Yan-Yi²,

Zhao-Shuo³

et al. 2023

Fractals

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Diabetes is a chronic disease that poses a serious threat to health, and its early risk prediction has been a hot research topic in the field of medical artificial intelligence. Routine medical checkups are the most common way to monitor people’s health status, and the data from medical checkups contain rich diagnostic information, which is valuable for diabetes risk prediction. Currently, most of the available studies on diabetes risk prediction are based on publicly available datasets, and the models and algorithms do not work well on real clinical datasets. Real routine checkup data are characterized by complex information, diverse features, high redundancy and poor balance, which pose great challenges for diabetes risk prediction. To address this problem, this paper proposes a multi-strategy data augmentation-based diabetes risk prediction method, after completing data pre-processing and feature selection, a counterfactual-based data balancing strategy is used to augment a minority class of instances, and a density clustering-based supplemental counterfactual data augmentation strategy is proposed to address the problem of insufficient representation of generated instances in the counterfactual method. Moreover, the uncertainty-weighted method is used in the model training phase. Based on the real checkup dataset, five machine learning methods including Logistic Regression (LR), SVM, Decision Tree, Random Forest and Gradient Boosting are used to model and use 5-fold cross-validation to carry out diabetes risk assessment and prediction. The experimental results showed that the sensitivity and precision of the models were significantly improved compared with the existing methods, and the sensitivity of the LR model for diabetes risk prediction on the real routine checkup dataset reached more than 90[Formula: see text], which meet the requirements of clinical application.

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Early diagnosis of diabetes mellitus using data mining and machine learning techniques

Kumar¹,

Deepa²

2023

IFS

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The remarkable developments in biotechnology as well as the health sciences have resulted in the production of an enormous amount of data, including high-throughput screening genomics information and clinical information obtained through extensive electronic health records (EHRs). The application of data mining and machine learning techniques in the biosciences is today more vital than ever to achieving this objective as attempts are made to intelligently translate all readily available data into knowledge. Diabetes mellitus (DM), a group of metabolic disorders, is well known to have a serious detrimental effect on population lives all over the world. Large-scale research into all aspects of diabetic has resulted in the production of enormous amounts of data (detection, etiopathophysiology, therapy, etc.). The goal of the current study is to conduct a thorough examination of the use of machine learning, data mining methods and tools in the field of diabetes research, with the first classification making an appearance to be the most popular. These applications relate to a Statistical model and Diagnosis, b) Diabetic Complications, c) Multiple genes Background and Environment, and e) Free Healthcare and Management. Numerous machine learning algorithms were applied. 85% of the methods used were supervised learning approaches, whereas 15% were uncontrolled ones, including association rules. Developed on improved support vector machines, the most successful and widely used algorithm (SVM). Medical datasets were predominantly used in terms of data kind.

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Machine learning based pattern detection technique for diabetes mellitus prediction

Cited by 3 publications

References 28 publications

AWD-stacking: An enhanced ensemble learning model for predicting glucose levels

AWD-stacking: An enhanced ensemble learning model for predicting glucose levels

A Diabetes Risk Predicting Method With Multi-Strategy Counterfactual-Based Data Augmentation

Early diagnosis of diabetes mellitus using data mining and machine learning techniques

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