Artificial Intelligence in Current Diabetes Management and Prediction

Nomura, Akihiro; Noguchi, Masahiro; Kometani, Mitsuhiro; Furukawa, Kazuhiro; Yoneda, Takashi

doi:10.1007/s11892-021-01423-2

Cited by 74 publications

(49 citation statements)

References 16 publications

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“…Use of machine learning for diabetes care has been mainly categorized into five parts: (1) early detection of diabetic retinopathy; (2) insulin treatment support (mainly continuous glucose monitoring); (3) patient self-management tools; (4) risk stratification; and (5) decision-making support tools for antihyperglycemic drug treatment for clinicians [32][33][34]. In this review, (1) to (4) will be briefly discussed and (5) will be discussed in detail.…”

Section: Machine Learning For Diabetes Carementioning

confidence: 99%

Machine Learning Approach to Drug Treatment Strategy for Diabetes Care

Fujihara

Sone

2023

Diabetes Metab J

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Globally, the number of people with diabetes mellitus has quadrupled in the past three decades, and approximately one in 11 adults worldwide have diabetes mellitus. Since both microvascular and macrovascular diseases in patients with diabetes predispose them to a lower quality of life as well as higher rates of mortality, managing blood glucose levels is of clinical relevance in diabetes care. Many classes of antihyperglycemic drugs are currently approved to treat hyperglycemia in patients with type 2 diabetes mellitus, with several new drugs having been developed during the last decade. Diabetes-related complications have been reduced substantially worldwide. Prioritization of therapeutic agents varies according to national guidelines. However, since the characteristics of participants in clinical trials differ from patients in actual clinical practice, it is difficult to apply the results of such trials to clinical practice. Machine learning approaches became highly topical issues in medicine along with rapid technological innovations in the fields of information and communication in the 1990s. However, adopting these technologies to support decisionmaking regarding drug treatment strategies for diabetes care has been slow. This review summarizes data from recent studies on the choice of drugs for type 2 diabetes mellitus focusing on machine learning approaches.

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Section: Machine Learning For Diabetes Carementioning

confidence: 99%

Machine Learning Approach to Drug Treatment Strategy for Diabetes Care

Fujihara

Sone

2023

Diabetes Metab J

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“…31 In particular, researchers have demonstrated the widespread application of machine learning and artificial intelligence to improve diabetes management. 32 The T1DX-QI also harnessed the growing power of big data by expanding the functionality of innovative benchmarking software. The T1DX QI Portal uses electronic medical record data of diabetes patients for clinic-to-clinic benchmarking and data analysis, using business intelligence solutions.…”

Section: Real-world Data and Disease Surveillancementioning

confidence: 99%

Diabetes Population Health Innovations in the Age of COVID-19: Insights From the T1D Exchange Quality Improvement Collaborative

Mungmode¹

2022

JCOM

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“…Te equation represents the number of trees that can be used in the model depending on the count of instances in the dataset used. When compared with GB, LGBM is comparatively faster and the parameters used are diferent that can further increase or decrease the efciency [26].…”

Section: Light_gradient_boosting_machine (Lgbm) Te Evaluation Ofmentioning

confidence: 99%

Diabetes Mellitus Disease Prediction and Type Classification Involving Predictive Modeling Using Machine Learning Techniques and Classifiers

Ahamed

Arya

Sangeetha

et al. 2022

Applied Computational Intelligence and Soft Computing

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The Diabetes-Mellitus (DM) disease is considered a persistent ailment that is triggered by excessive sugar levels in the blood of a person. It gives rise to severe health complications when left untreated and can also give rise to related diseases such as cardiac attack, nervous damage, foot problems, liver and kidney damage, and eye problems. These problems are caused by a series of factors interrelated to one another such as age, gender, family history, BMI, and Blood Glucose. Various Machine-Learning (ML) algorithms are being used in order to predict and detect the disease to avoid further complications of health. The Diabetes prediction process can be further improvised by identifying the type a person is being affected by and the probability of the occurrence of the related diseases. In order to perform the mentioned task, two types of the dataset are used in the study, namely, PIMA and a clinical survey dataset. Various ML algorithms such as Random Forest, Light Gradient Boosting Machine, Gradient Boosting Machine, Support Vector Machine, Decision Tree, and XGBoost are being used. The performance metrics used are accuracy, precision, recall, specificity, and sensitivity. Techniques such as Data Augmentation and Sampling are used. In comparison with the research conducted previously, the paper focuses on improvisation of the accuracy with a percentage of 95.20 using the LGBM Classifier, and Diabetes is also classified as Prediabetes or Diabetes using many Classification mechanisms.

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Artificial Intelligence in Current Diabetes Management and Prediction

Cited by 74 publications

References 16 publications

Machine Learning Approach to Drug Treatment Strategy for Diabetes Care

Machine Learning Approach to Drug Treatment Strategy for Diabetes Care

Diabetes Population Health Innovations in the Age of COVID-19: Insights From the T1D Exchange Quality Improvement Collaborative

Diabetes Mellitus Disease Prediction and Type Classification Involving Predictive Modeling Using Machine Learning Techniques and Classifiers

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