The diagnosis of diabetes, a prevalent global health condition, is crucial for preventing severe complications.In recent years, there has been a growing effort to develop intelligent diagnostic systems for diabetes utilizing machine learning (ML) algorithms.Despite these efforts, achieving high accuracy rates using such systems remains a significant challenge.Recent advancements in ensemble ML methods offer promising opportunities for early detection of diabetes, as they are known to be faster and more cost-effective than traditional approaches.Therefore, this study proposes a practical framework for diagnosing diabetes that involves three stages.The data preprocessing stage encompasses several crucial tasks, including handling missing values, identifying outliers, balancing the data, normalizing the data, and selecting relevant features.Subsequently, the hyperparameters of the ML algorithms are fine-tuned using grid search to improve their performance.In the final stage, the framework employs ensemble techniques such as bagging, boosting, and stacking to combine multiple ML algorithms and further enhance their predictive capability.Pima Indians Diabetes Database open-access dataset was used to test the performance of the proposed models.The experimental results of this framework indicate the superiority of ensemble methods in diagnosing diabetes compared to individual ML models.The stacking method achieved the best accuracy among the ensemble methods, with the stacked random forest (RF) and support vector machine (SVM) model attaining an accuracy of 97.50%.Among the bagging methods, the RF model yielded the highest accuracy, while among the boosting methods, eXtreme Gradient Boosting (XGB) model achieved the highest accuracy rates of 97.20% and 97.10%, respectively.Moreover, our proposed framework outperforms other ML models as confirmed by the comparison.The study has demonstrated that ensemble methods are crucial for accurate diabetes diagnosis, enabling early detection through efficient preprocessing and calibrated models. 

A practical framework for early detection of diabetes using ensemble machine learning models

For a long time, the use of steady state method to detect the thermal insulation performance of vacuum glass caused some problems such as long detection period, many influencing factors, inaccurate detection, etc. In order to improve the efficiency of vacuum glass insulation performance detection and reduce the cost of vacuum glass industrialization, the ensemble learning method for rapid detection of vacuum glass insulation performance is studied. Firstly, the correlation between variables and the distribution of variables are analyzed based on unsteady state method. The temperature-related variables and heat transfer coefficient are used as the input variables and target variables of the model. Then, three models and one model are selected as the first and second layers of stacking model based on five-fold cross-validation and Spearman correlation analysis. Finally, the heat transfer coefficient characterizing the thermal insulation performance of vacuum glass is predicted by the designed 3 + 1 stacking model. We involve 10 single models and other 11 ensemble models to verify the effectiveness of the method. The experimental results show that the 3 + 1 stacking model based on five-fold cross-validation and Spearman correlation analysis has the best prediction effect, which outperforms single model and other ensemble models. It improves the generalization ability of prediction model. 

Rapid detection method for insulation performance of vacuum glass based on ensemble learning

Diabetes is a chronic disease, which is characterized by abnormally high blood sugar levels. It may affect various organs and tissues, and even lead to life-threatening complications. Accurate prediction of diabetes can significantly reduce its incidence. However, the current prediction methods struggle to accurately capture the essential characteristics of nonlinear data, and the black-box nature of these methods hampers its clinical application. To address these challenges, we propose KCCAM_DNN, a diabetes prediction method that integrates Kendall’s correlation coefficient and an attention mechanism within a deep neural network. In the KCCAM_DNN, Kendall’s correlation coefficient is initially employed for feature selection, which effectively filters out key features influencing diabetes prediction. For missing values in the data, polynomial regression is utilized for imputation, ensuring data completeness. Subsequently, we construct a deep neural network (KCCAM_DNN) based on the self-attention mechanism, which assigns greater weight to crucial features affecting diabetes and enhances the model’s predictive performance. Finally, we employ the SHAP model to analyze the impact of each feature on diabetes prediction, augmenting the model’s interpretability. Experimental results show that KCCAM_DNN exhibits superior performance on both PIMA Indian and LMCH diabetes datasets, achieving test accuracies of 99.090% and 99.333%, respectively, approximately 2% higher than the best existing method. These results suggest that KCCAM_DNN is proficient in diabetes prediction, providing a foundation for informed decision-making in the diagnosis and prevention of diabetes. 

A deep neural network prediction method for diabetes based on Kendall’s correlation coefficient and attention mechanism

This study incorporates two unique machine learning algorithms, Huber regression and Light Gradient Boosting Machines (LGBM), for estimating ship consumption of fuel. These methods are employed to create forecasting models for ship fuel consumption during journeys, which is especially useful when interacting with non-linear data. The study then analyzes and evaluates the prediction accuracy of these two approaches compared to a baseline model generated using linear regression. The results of the investigation show that both methods establish extremely accurate predictions while handling non-linear data quickly. However, the Huber-based model outperforms the LGBM in terms of prediction accuracy, with an R-squared value of 0.979 versus 0.917 for the LGBM. In addition, the Huber-based model has a diminished prediction error, with an RMSE of 2.278, compared to the LGBM model's RMSE of 4.55. The graphical methods of the violin plot and Taylor's diagram further established the superiority of Huber ML. These findings imply that Huber regression could be a suitable option for estimating in-route ship fuel usage in real time. As a consequence, this study emphasises the potential benefits of machine learning for accurately predicting ship fuel consumption, providing encouraging possibilities to optimise fuel usage while lowering greenhouse gas emissions. 

Development of comprehensive models for precise prognostics of ship fuel consumption

Diabetes is a condition associated with high levels of blood glucose and insulin deficiency that is becoming more prevalent among children and adolescents. It is the ninth leading cause of mortality globally at over 1 million deaths per year. Misdiagnosis of type 1 diabetes with type 2 are frequent and causes diabetic ketoacidosis, a critical complication. This paper seeks for the most accurate model between frequently used machine learning algorithms - Multilayer Perceptron, Support Vector Machine and Random Forest in predicting diabetes among patients with focus on blood content analysis, specifically using the Laboratory of Medical City Hospital (LMCH) Diabetes Dataset, which was retrieved from Mendeley. Using hyperparameter optimization and ANOVA F- Value feature scaling, results show that Random Forest produced the highest accuracy at 1.00, followed by Multilayer Perceptron at 0.987 and Support Vector Machine at 0.96. 

Using Machine Learning for the Prediction of Diabetes with Emphasis on Blood Content

Abstract Machine learning helps construct predictive models in clinical data analysis, predicting stock prices, picture recognition, financial modelling, disease prediction, and diagnostics. This paper proposes machine learning ensemble algorithms to forecast diabetes. The ensemble combines k-NN, Naive Bayes (Gaussian), Random Forest (RF), Adaboost, and a recently designed Light Gradient Boosting Machine. The proposed ensembles inherit detection ability of LightGBM to boost accuracy. Under fivefold cross-validation, the proposed ensemble models perform better than other recent models. The k -NN, Adaboost, and LightGBM jointly achieve 90.76% detection accuracy. The receiver operating curve analysis shows that $$k$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>k</mml:mi> </mml:math> -NN, RF, and LightGBM successfully solve class imbalance issue of the underlying dataset. 

/pdf/an-ensemble-of-light-gradient-boosting-machine-and-adaptive-2munxenj.pdf

An Ensemble of Light Gradient Boosting Machine and Adaptive Boosting for Prediction of Type-2 Diabetes

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An Ensemble of Light Gradient Boosting Machine and Adaptive Boosting for Prediction of Type-2 Diabetes