Early Risk Prediction of Diabetes Based on GA-Stacking

Tan, Yuting; Chen, He; Zhang, Jianjun; Tang, Ruichun; Liu, Peishun

doi:10.3390/app12020632

Cited by 20 publications

(15 citation statements)

References 17 publications

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“…The fully connected network integrated the extracted features, and the output layer made the prediction. In this liquefication dataset, MLP took advantage of the hidden nodes, weighted sum, and gradient computation, but also explored the relationship between the input features in the higher dimensional space efficiently [ 39 ]. The role of the kernel function for mapping the features into the higher dimensional space helps generate the support vector formation in SVR.…”

Section: Methodsmentioning

confidence: 99%

A Stacked Generalization Model to Enhance Prediction of Earthquake-Induced Soil Liquefaction

Preethaa

Natarajan

Rathinakumar³

et al. 2022

Sensors

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Earthquakes cause liquefaction, which disturbs the design phase during the building construction process. The potential of earthquake-induced liquefaction was estimated initially based on analytical and numerical methods. The conventional methods face problems in providing empirical formulations in the presence of uncertainties. Accordingly, machine learning (ML) algorithms were implemented to predict the liquefaction potential. Although the ML models perform well with the specific liquefaction dataset, they fail to produce accurate results when used on other datasets. This study proposes a stacked generalization model (SGM), constructed by aggregating algorithms with the best performances, such as the multilayer perceptron regressor (MLPR), support vector regression (SVR), and linear regressor, to build an efficient prediction model to estimate the potential of earthquake-induced liquefaction on settlements. The dataset from the Korean Geotechnical Information database system and the standard penetration test conducted on the 2016 Pohang earthquake in South Korea were used. The model performance was evaluated by using the R2 score, mean-square error (MSE), standard deviation, covariance, and root-MSE. Model validation was performed to compare the performance of the proposed SGM with SVR and MLPR models. The proposed SGM yielded the best performance compared with those of the other base models.

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Section: Methodsmentioning

confidence: 99%

A Stacked Generalization Model to Enhance Prediction of Earthquake-Induced Soil Liquefaction

Preethaa

Natarajan

Rathinakumar³

et al. 2022

Sensors

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“…The anticipated model obtained an accuracy of 94.5%. According to Tan et al [22] anticipated a genetic algorithm (GA)-stacking ensemble learning model for the prediction of Diabetes. GA based on DT was used for feature selection.…”

Section: Hybrid ML Techniquesmentioning

confidence: 99%

Comparative analysis of predictive machine learning algorithms for diabetes mellitus

Kangra

Singh

2023

Bulletin EEI

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Diabetes mellitus (DM) is a serious worldwide health issue, and its prevalence is rapidly growing. It is a spectrum of metabolic illnesses defined by perpetually increased blood glucose levels. Undiagnosed diabetes can lead to a variety of problems, including retinopathy, nephropathy, neuropathy, and other vascular abnormalities. In this context, machine learning (ML) technologies may be particularly useful for early disease identification, diagnosis, and therapy monitoring. The core idea of this study is to identify the strong ML algorithm to predict it. For this several ML algorithms were chosen i.e., support vector machine (SVM), Naïve Bayes (NB), K nearest neighbor (KNN), random forest (RF), logistic regression (LR), and decision tree (DT), according to studied work. Two, Pima Indian diabetic (PID) and Germany diabetes datasets were used and the experiment was performed using Waikato environment for knowledge analysis (WEKA) 3.8.6 tool. This article discussed about performance matrices and error rates of classifiers for both datasets. The results showed that for PID database (PIDD), SVM works better with an accuracy of 74% whereas for Germany KNN and RF work better with 98.7% accuracy. This study can aid healthcare facilities and researchers in comprehending the value and application of ML algorithms in predicting diabetes at an early stage.

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“…, availability of green space, residential noise level, traffic, and proximity to roads, air pollution, as well as environmental conditions, safety, and other environmental characteristics. Early prediction of diabetes can make it easier for doctors and patients to intervene as soon as possible so that the risk of complications can be reduced [8].…”

Section: Introductionmentioning

confidence: 99%

“…Many studies have been conducted to predict diabetes using various machine learning algorithms, including by [1], [3], [8]- [12]. Research by [3] predicts diabetes using several machine learning algorithms such as Naïve Bayes, Random Forest, Support Vector Machine, and Multilayer Perceptron.…”

Section: Introductionmentioning

confidence: 99%

“…Research by [9] predicts diabetes using several supervised learning algorithms, including Logistic Regression, Neural Network, Random Forest, kNN, Tree, SVM, Naïve Bayes, and AdaBoost. Research by [8] predicts early risk for diabetes using the GA-Stacking Algorithm. This study shows a comparison of the performance of several supervised learning algorithms with GA-Stacking.…”

Section: Introductionmentioning

confidence: 99%

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Diabetes Risk Prediction Using Extreme Gradient Boosting (XGBoost)

Wardhani

Akbar

2022

join

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One of the uses of medical data from diabetes patients is to produce models that can be used by medical personnel to predict and identify diabetes in patients. Various techniques are used to be able to provide a diabetes model as early as possible based on the symptoms experienced by diabetic patients, including using machine learning. The machine learning technique used to predict diabetes in this study is extreme gradient boosting (XGBoost). XGBoost is an advanced implementation of gradient boosting along with multiple regularization factors to accurately predict target variables by combining simpler and weaker model set estimations. Errors made by the previous model are tried to be corrected by the next model by adding some weight to the model. The diabetes prediction model using XGBoost is shown in the form of a tree, with the accuracy of the model produced in this study of 98.71%

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Early Risk Prediction of Diabetes Based on GA-Stacking

Cited by 20 publications

References 17 publications

A Stacked Generalization Model to Enhance Prediction of Earthquake-Induced Soil Liquefaction

A Stacked Generalization Model to Enhance Prediction of Earthquake-Induced Soil Liquefaction

Comparative analysis of predictive machine learning algorithms for diabetes mellitus

Diabetes Risk Prediction Using Extreme Gradient Boosting (XGBoost)

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