Diagnosis of Diabetes Mellitus Using Gradient Boosting Machine (LightGBM)

Rufo, Derara Duba; Debelee, Taye Girma; Ibenthal, Achim; Negera, Worku Gachena

doi:10.3390/diagnostics11091714

Cited by 129 publications

(69 citation statements)

References 44 publications

(55 reference statements)

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“…To our knowledge, this was the first study that targeted the elderly population (≥65 years) in China to build predictive models for diabetes using machine learning techniques, which would have great implications for designing diabetes prevention focusing on the elderly. With the development of artificial intelligence, machine learning techniques have been widely applied in the medical field, especially for prediction models for diabetes [ 49 , 51 , 53 , 56 , 57 , 58 ]. It is worth noting that the advantages of machine learning models are well-documented empirically compared with traditional statistical methods, but its disadvantage is the lack of model interpretability [ 13 ].…”

Section: Discussionmentioning

confidence: 99%

Predicting the Risk of Incident Type 2 Diabetes Mellitus in Chinese Elderly Using Machine Learning Techniques

Liu

Zhang

et al. 2022

JPM

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Early identification of individuals at high risk of diabetes is crucial for implementing early intervention strategies. However, algorithms specific to elderly Chinese adults are lacking. The aim of this study is to build effective prediction models based on machine learning (ML) for the risk of type 2 diabetes mellitus (T2DM) in Chinese elderly. A retrospective cohort study was conducted using the health screening data of adults older than 65 years in Wuhan, China from 2018 to 2020. With a strict data filtration, 127,031 records from the eligible participants were utilized. Overall, 8298 participants were diagnosed with incident T2DM during the 2-year follow-up (2019–2020). The dataset was randomly split into training set (n = 101,625) and test set (n = 25,406). We developed prediction models based on four ML algorithms: logistic regression (LR), decision tree (DT), random forest (RF), and extreme gradient boosting (XGBoost). Using LASSO regression, 21 prediction features were selected. The Random under-sampling (RUS) was applied to address the class imbalance, and the Shapley Additive Explanations (SHAP) was used to calculate and visualize feature importance. Model performance was evaluated by the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy. The XGBoost model achieved the best performance (AUC = 0.7805, sensitivity = 0.6452, specificity = 0.7577, accuracy = 0.7503). Fasting plasma glucose (FPG), education, exercise, gender, and waist circumference (WC) were the top five important predictors. This study showed that XGBoost model can be applied to screen individuals at high risk of T2DM in the early phrase, which has the strong potential for intelligent prevention and control of diabetes. The key features could also be useful for developing targeted diabetes prevention interventions.

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Section: Discussionmentioning

confidence: 99%

Predicting the Risk of Incident Type 2 Diabetes Mellitus in Chinese Elderly Using Machine Learning Techniques

Liu

Zhang

et al. 2022

JPM

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“…It is suggested that the LightGBM model has better predictive performance for the classification of etiological types of patients with classic FUO. LightGBM is a distributed gradient lifting framework based on a decision tree algorithm, which has high efficiency and performance in dealing with binary classifications and multi-classification problems (44)(45)(46). LightGBM is an ensemble algorithm developed by Microsoft, which is superior to other machine learning methods for disease diagnosis in many cases (45).…”

Section: Discussionmentioning

confidence: 99%

“…LightGBM is a distributed gradient lifting framework based on a decision tree algorithm, which has high efficiency and performance in dealing with binary classifications and multi-classification problems (44)(45)(46). LightGBM is an ensemble algorithm developed by Microsoft, which is superior to other machine learning methods for disease diagnosis in many cases (45). Fundamentally, this is achieved by combining multiple base classifiers into an ensemble model by learning the inherent statistics of the combined classifiers and, hence, outperforming the single classifiers.…”

Section: Discussionmentioning

confidence: 99%

Application of Machine Learning for the Prediction of Etiological Types of Classic Fever of Unknown Origin

Yan

Chen

Liu

et al. 2021

Front. Public Health

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Background: The etiology of fever of unknown origin (FUO) is complex and remains a major challenge for clinicians. This study aims to investigate the distribution of the etiology of classic FUO and the differences in clinical indicators in patients with different etiologies of classic FUO and to establish a machine learning (ML) model based on clinical data.Methods: The clinical data and final diagnosis results of 527 patients with classic FUO admitted to 7 medical institutions in Chongqing from January 2012 to August 2021 and who met the classic FUO diagnostic criteria were collected. Three hundred seventy-three patients with final diagnosis were divided into 4 groups according to 4 different etiological types of classical FUO, and statistical analysis was carried out to screen out the indicators with statistical differences under different etiological types. On the basis of these indicators, five kinds of ML models, i.e., random forest (RF), support vector machine (SVM), Light Gradient Boosting Machine (LightGBM), artificial neural network (ANN), and naive Bayes (NB) models, were used to evaluate all datasets using 5-fold cross-validation, and the performance of the models were evaluated using micro-F1 scores.Results: The 373 patients were divided into the infectious disease group (n = 277), non-infectious inflammatory disease group (n = 51), neoplastic disease group (n = 31), and other diseases group (n = 14) according to 4 different etiological types. Another 154 patients were classified as undetermined group because the cause of fever was still unclear at discharge. There were significant differences in gender, age, and 18 other indicators among the four groups of patients with classic FUO with different etiological types (P < 0.05). The micro-F1 score for LightGBM was 75.8%, which was higher than that for the other four ML models, and the LightGBM prediction model had the best performance.Conclusions: Infectious diseases are still the main etiological type of classic FUO. Based on 18 statistically significant clinical indicators such as gender and age, we constructed and evaluated five ML models. LightGBM model has a good effect on predicting the etiological type of classic FUO, which will play a good auxiliary decision-making function.

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“…The gradient boosting machine (GBM), a machine learning algorithm for regression and classification, can train many models in order. Then, each new model updates the prediction using the gradient descent method [ 16 – 20 ]. GBM has shown great success in a wide range of practical applications and can be highly customized to the application's specific needs, gradually minimizing the loss of function.…”

Section: Introductionmentioning

confidence: 99%

Logistic Model and Gradient Boosting Machine Model for Physical Therapy of Lumbar Disc Herniation

Zhao

Xue

et al. 2022

Computational and Mathematical Methods in Medicine

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Objective. Physical therapy is a common clinical treatment for patients with lumbar disc herniation. The study is aimed at exploring the feasibility of mathematical expression and curative effect prediction of physical therapy in patients with lumbar disc herniation using a logistic model and gradient boosting machine (GBM). Methods. A total of 142 patients with lumbar disc herniation were treated with physical therapy. The pain was evaluated by the visual analogue scale (VAS) before each treatment. The logistic model was used to conduct a global regression analysis on patients with lumbar disc herniation. The final results of the whole course of treatment were predicted by the measured values of 2-9 times of treatment. The GBM model was used to predict and analyze the curative effect of physical therapy. Results. The mathematical expression ability of the logistic regression model for patients with lumbar disc herniation undergoing physical therapy was sufficient, and the global determination coefficient was 0.721. The results would be better for more than five measurements. The AUC of GBM mode logistic regression analysis was 0.936 and 0.883, and the prediction effect is statistically significant. Conclusion. Both the logistic and GBM model can fully express the changes in patients with lumbar disc herniation during physical therapy.

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Diagnosis of Diabetes Mellitus Using Gradient Boosting Machine (LightGBM)

Cited by 129 publications

References 44 publications

Predicting the Risk of Incident Type 2 Diabetes Mellitus in Chinese Elderly Using Machine Learning Techniques

Predicting the Risk of Incident Type 2 Diabetes Mellitus in Chinese Elderly Using Machine Learning Techniques

Application of Machine Learning for the Prediction of Etiological Types of Classic Fever of Unknown Origin

Logistic Model and Gradient Boosting Machine Model for Physical Therapy of Lumbar Disc Herniation

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