Quantifying health-related quality of life in Malaysian type 2 diabetes: focusing on complication types and severity

Tan, Michelle Hwee Pheng; Ong, Siew Chin; Thakumar, Annushiah Vasan; Mustafa, Norlaila

doi:10.1007/s11136-023-03360-x

Cited by 5 publications

(2 citation statements)

References 65 publications

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“…Endocrine disorders can further affect human metabolic processes and lead to a series of complications. 70 , 71 Metabolic diseases are often resulted from the abnormal accumulation or degradation of metabolisms. Nowadays, endocrine and metabolic diseases are prevalent and impose a significant burden on public health, and the long-term monitoring of these diseases is necessary.…”

Section: Wearable Sensor Applications In Human Body Systemsmentioning

confidence: 99%

Clinical applications of smart wearable sensors

Tao¹,

Liu²,

Zhang³

et al. 2023

iScience

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Section: Wearable Sensor Applications In Human Body Systemsmentioning

confidence: 99%

Clinical applications of smart wearable sensors

Tao¹,

Liu²,

Zhang³

et al. 2023

iScience

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“…Chronic illness is a disease or condition that is ongoing or the effects of which are permanent [ 1 , 2 ]. However, the consequences of this type of disease can have various negative effects on the quality of life, and a large part of the national budget is spent on chronic diseases [ 3 , 4 ]. Diabetes is one of those chronic diseases that pose a major health risk and the number of medical causes of death is increasing every year, making it one of the biggest problems in emerging and developed countries [ 5 , 6 ] High blood sugar levels have been linked to diabetes.…”

Section: Introductionmentioning

confidence: 99%

Prediction of diabetes disease using an ensemble of machine learning multi-classifier models

Abnoosian,

Farnoosh,

Behzadi

2023

BMC Bioinformatics

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Background and objective Diabetes is a life-threatening chronic disease with a growing global prevalence, necessitating early diagnosis and treatment to prevent severe complications. Machine learning has emerged as a promising approach for diabetes diagnosis, but challenges such as limited labeled data, frequent missing values, and dataset imbalance hinder the development of accurate prediction models. Therefore, a novel framework is required to address these challenges and improve performance. Methods In this study, we propose an innovative pipeline-based multi-classification framework to predict diabetes in three classes: diabetic, non-diabetic, and prediabetes, using the imbalanced Iraqi Patient Dataset of Diabetes. Our framework incorporates various pre-processing techniques, including duplicate sample removal, attribute conversion, missing value imputation, data normalization and standardization, feature selection, and k-fold cross-validation. Furthermore, we implement multiple machine learning models, such as k-NN, SVM, DT, RF, AdaBoost, and GNB, and introduce a weighted ensemble approach based on the Area Under the Receiver Operating Characteristic Curve (AUC) to address dataset imbalance. Performance optimization is achieved through grid search and Bayesian optimization for hyper-parameter tuning. Results Our proposed model outperforms other machine learning models, including k-NN, SVM, DT, RF, AdaBoost, and GNB, in predicting diabetes. The model achieves high average accuracy, precision, recall, F1-score, and AUC values of 0.9887, 0.9861, 0.9792, 0.9851, and 0.999, respectively. Conclusion Our pipeline-based multi-classification framework demonstrates promising results in accurately predicting diabetes using an imbalanced dataset of Iraqi diabetic patients. The proposed framework addresses the challenges associated with limited labeled data, missing values, and dataset imbalance, leading to improved prediction performance. This study highlights the potential of machine learning techniques in diabetes diagnosis and management, and the proposed framework can serve as a valuable tool for accurate prediction and improved patient care. Further research can build upon our work to refine and optimize the framework and explore its applicability in diverse datasets and populations.

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