Diagnosis of diabetes mellitus using artificial neural network and classification and regression tree optimized with genetic algorithm

Özmen, Ebru Pekel; Özcan, Tuncay

doi:10.1002/for.2652

Cited by 15 publications

(12 citation statements)

References 26 publications

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“…The Gini index of each attribute can be calculated using Equation 3.

{Gini}_{A} ((), T) = \frac{(||, T_{1})}{|T|} italicGini ((), T_{1}) + \frac{(||, T_{2})}{|T|} italicGini ((), T_{2})

The attribute that has the maximum of the degradation in impurity is picked as the splitting attribute (Pekel Özmen & Özcan, 2020). The reduction in impurity is obtained from Equation 4.…”

Section: Methodsmentioning

confidence: 99%

A novel deep learning model based on convolutional neural networks for employee churn prediction

Özmen

Özcan

2021

Journal of Forecasting

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Employees are one of the most important resources of a company. The churn of valuable employees significantly affects a company's performance. The design of systems that predict employee churn is critical importance for companies. At this point, machine learning algorithms offer important opportunities for the diagnosis of employee churn. Nowadays, traditional classification algorithms have been replaced by deep learning models. In this study, firstly, a convolutional neural network (CNN) model was applied on a numerical data set for employee churn prediction in retailing. Later, because the data loss is too much in data transformations, a new hybrid extended convolutional decision tree model (ECDT) was proposed by improving the CNN algorithm. Finally, a novel model (ECDT‐GRID) was developed by applying grid search optimization to improve the classification accuracy of ECDT. Numerical results showed that the developed ECDT‐GRID model outperformed the CNN and ECDT models and basic classification algorithms in terms of classification accuracy, and this model provided an efficient methodology for prediction of employee churn.

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“…The Gini index of each attribute can be calculated using Equation 3.

{Gini}_{A} ((), T) = \frac{(||, T_{1})}{|T|} italicGini ((), T_{1}) + \frac{(||, T_{2})}{|T|} italicGini ((), T_{2})

The attribute that has the maximum of the degradation in impurity is picked as the splitting attribute (Pekel Özmen & Özcan, 2020). The reduction in impurity is obtained from Equation 4.…”

Section: Methodsmentioning

confidence: 99%

A novel deep learning model based on convolutional neural networks for employee churn prediction

Özmen

Özcan

2021

Journal of Forecasting

Self Cite

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“…The null values (0) of the Indian PIMA, Glucose (5), BlooPressure(35), SkinThickness(227), Insulin (374) and BMI (11) dataset characteristics are identified as missing values (Table 3). But this study was conducted with data containing missing values in their original state, considered as null values.…”

Section: Presentation Of the Datasetmentioning

confidence: 99%

“…This may explain the fact that these algorithms outperform others in comparative studies. The results of better estimators that are DT in [11]RF in [12], Adaboost in [5], LightGBM in [16] and XGBoost in [13]give us comfort in the choice of tree methods to conduct our study. As the aim of the study is to detect almost all diabetics and minimize the false negative rate, we prefer the classifier that achieves good sensitivity.…”

Section: K-fold (K=12)mentioning

confidence: 99%

“…In many studies, decision tree methods outperform other techniques as best estimators. The authors [11] found that decision trees estimate better than neural networks, with an accuracy of 83%. The same is true for the study [12] which found that Random Forest (RF) with 98.48% accuracy, was voted the best estimator among the following methods: Support Vector Machines, K-Nearest Neighbor, Naïve Bayes, Gradient boosting and Logistic Regression.…”

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confidence: 98%

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Predictive Analysis of Diabetes Without Data Pre-Processing via the Evaluation of Tree Algorithms

Kolo¹,

Edwige²,

Hyacinthe³

et al. 2022

IJAR

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Diabetes is a common disease, incurable and fatal in its complication phases. Its management, like many other metabolic diseases, remains a scientific challenge. Mathematical approaches have been used to understand this scourge and artificial intelligence is used to model its prediction. In general, the effectiveness and efficiency of an artificial intelligence solution depends on the nature and characteristics of the data and the performance of the learning methods. Hence the interest in the quality of the data and the performance of the methods used to model such a task. In order to find a suitable artificial intelligence model for diabetes prediction, several studies have used methods from different techniques. Thus, diabetes prediction has been addressed using machine learning methods, neural networks, deep learning, Bayesian naive classification, K-nearest neighbors and machine vector support. In order to compare the performance to determine the best model, several of these methods are analyzed in previous studies. Thus, this paper evaluates the methods based on the decision tree technique (DT, RF, LightGBM, Adaboost and XGBoost), based on the PIMA Diabetes Indian data (PID). The aim is to show the predictive ability of the methods of this technique and to determine the appropriate method for predicting diabetes with raw data. The PIMA data are described statistically, and the comparative analysis of the models is performed following K-fold cross-validation, before and after class balancing. At the end of the experiment, the best results are obtained by LightGBM, XGBoostand RF on different metrics.

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“…Neural networks are widely applied in various types of classification and regression tasks (Borovkova & Tsiamas, 2019; Bucci, 2020; Kraft et al, 2020; Pekel Ozmen & Ozcan, 2020) because of their high learning capability and strong approximation ability for nonlinear series. However, iterative learning methods, such as backpropagation (BP) neural network, radial basis functional (RBF) network, long short‐term memory (LSTM), and others, have the shortcomings of long training time (Tang et al, 2018), easily falling into local optima, and slow convergence speeds (Chitsazan et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Stochastic configuration network based on improved whale optimization algorithm for nonstationary time series prediction

Chen

Xiao

Wang

et al. 2022

Journal of Forecasting

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The stochastic configuration network (SCN), a type of randomized learning algorithm, can solve the infeasible problem in random vector functional link (RVFL) by establishing a supervisory mechanism. The advantages of fast learning, convergence and not easily falling into local optima make SCN popular. However, the prediction effect of SCN is affected by the parameter settings and the nonstationarity of input data. In this paper, a hybrid model based on variational mode decomposition (VMD), improved whale optimization algorithm (IWOA), and SCN is proposed. The SCN will predict relatively stable data after decomposition by VMD, and parameters of SCN are optimized by IWOA. The IWOA diversifies the initial population by employing logistic chaotic map based on bit reversal and improves the search ability by using Lévy flight. The exploration and exploitation of IWOA are superior to those of other optimization algorithms in multiple benchmark functions and CEC2020. Moreover, the proposed model is applied to the prediction of the nonstationary wind speeds in four seasons. We evaluate the performance of the proposed model using four evaluation indicators. The results show that the R 2 of the proposed model under four seasons are more than 0.999, and the root mean square error, mean absolute error, and symmetric mean absolute percentage error are less than 0.3, 0.17, and 13%, respectively, which are almost 1/10, 1/10, and 1/4 those of SCN, respectively.

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Diagnosis of diabetes mellitus using artificial neural network and classification and regression tree optimized with genetic algorithm

Cited by 15 publications

References 26 publications

A novel deep learning model based on convolutional neural networks for employee churn prediction

A novel deep learning model based on convolutional neural networks for employee churn prediction

Predictive Analysis of Diabetes Without Data Pre-Processing via the Evaluation of Tree Algorithms

Stochastic configuration network based on improved whale optimization algorithm for nonstationary time series prediction

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