A proposed approach for diabetes diagnosis using neuro-fuzzy technique

Alasaady, Maher Talal; Aris, Teh Noranis Mohd; Sharef, Nurfadhlina Mohd; Hamdan, Hazlina

doi:10.11591/eei.v11i6.4269

Cited by 6 publications

(7 citation statements)

References 28 publications

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“…Table 6 shows how our model with PID dataset outperforms all other existing models in terms of performance measures. The proposed model is evaluated against the most recent diabetes prediction models include (deep neural network [10], CART-GA, ANN-GA [8], Feedforward neural network [9], Artificial Neural Network+ Logistic Regression+DecisionTree [1], Random forest, J48 decision tree, Naïve Bayes, Naïve Bayes with feature selection three factors, and Naïve Bayes with feature selection five factors [25], DL, QML [13], DL [12], and ANFIS [14]). In terms of performance metrics, it showed that the suggested model performed better than these models.…”

Section: Resultsmentioning

confidence: 99%

“…The authors proved the DL techniques achieved high performance as compared with QML. Maher et al [14] suggest an approach to the diagnosis of diabetes relies on Adaptive Neuro-Fuzzy Inference System (ANFIS). This approach consists of several phases including, preprocessing phase, classification phase, and evaluation.…”

Section: Related Workmentioning

confidence: 99%

“…In this paper, applied Min-Max Normalization (MMN) that makes attribute values in the range [0,1] or [-1, 1]. Equation (1) shows the MMN as follows [14]:…”

Section: Data-normalizationmentioning

confidence: 99%

“…10-fold cross-validation has been used as a performance measure to evaluate our proposed model. The metrics used to determine the performance model include accuracy as in (14), recall as in (10), precision as in (12), specificity as in (11), and f1-score as in (13). The following equations refer to these metrics [24]:…”

Section: Experimental Studymentioning

confidence: 99%

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Enhanced the prediction approach of diabetes using an autoencoder with regularization and deep neural network

Ismael¹,

Al-A’araji²,

Shukur³

2023

PEN

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Diabetes mellitus is considered one of the foremost common and extreme diseases worldwide. A precise and early diagnosis of diabetes is essential to avoid complications and is of crucial importance to the medical care that patients get. To achieve that, we need to develop a model to predict diabetes. There are many prediction models, but they suffer from some problems such as the accuracy of prediction being poor and the time complexity. The prediction process is highly dependent on important features. So, in this paper, we proposed a new model called (CAER-DNN) that depends on an unsupervised technique for generating newly important features and a deep neural network for the prediction process. The unsupervised technique is called complete autoencoder with regularization techniques (CAER) that uses to reconstruct the original features (newly learned features). It is focused too much on training the most important learned features and misses out on less important features. Thus, improving the performance of the prediction process. These important features are used as input to the deep neural network for the prediction of diabetes. Our model is applied to two sets of data including Pima Indian and Mendeley diabetic datasets. Based on the 10-fold cross-validation technique Pima Indian dataset achieves high performance in evaluation measures (f1-score 97.38%, accuracy, recall 97.25%, specificity 97.59%, precision 97.53%,). While the Mendeley diabetes dataset achieved high performance in evaluation measures (f1-score 94.51%, accuracy 98.48, recall 91.74%, accuracy-balance 98.21%, precision 98.21%) based on the holdout technique. compared with other existing machine learning and deep learning techniques our model outperformed existing techniques.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…In this paper, applied Min-Max Normalization (MMN) that makes attribute values in the range [0,1] or [-1, 1]. Equation (1) shows the MMN as follows [14]:…”

Section: Data-normalizationmentioning

confidence: 99%

Section: Experimental Studymentioning

confidence: 99%

See 2 more Smart Citations

Enhanced the prediction approach of diabetes using an autoencoder with regularization and deep neural network

Ismael¹,

Al-A’araji²,

Shukur³

2023

PEN

View full text Add to dashboard Cite

show abstract

“…In addition, another research article [11], [12], [13] analyzed the effectiveness of several machine learning models such as KNN, DT, RF, Naïve Bayes (NB), support vector machine (SVM), and logistic regression. The comparison of the performance of these machine-learning models reveals that the logistic regression model outperforms other models with an accuracy score of 75.32%.…”

Section: Introductionmentioning

confidence: 99%

Early Prediction of Gestational Diabetes with Parameter-Tuned K-Nearest Neighbor Classifier

Assegie,

Suresh,

Purushothaman

et al. 2023

Journal of Robotics and Control

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Diabetes is one of the quickly spreading chronic diseases causing health complications, such as diabetes retinopathy, kidney failure, and cardiovascular disease. Recently, machine-learning techniques have been widely applied to develop a model for the early prediction of diabetes. Due to its simplicity and generalization capability, K-nearest neighbor (KNN) has been one of the widely employed machine learning techniques for diabetes prediction. Early diabetes prediction has a significant role in managing and preventing complications associated with diabetes, such as retinopathy, kidney failure, and cardiovascular disease. However, the prediction of diabetes in the early stage has remained challenging due to the accuracy and reliability of the KNN model. Thus, gird search hyperparameter optimization is employed to tune the K values of the KNN model to improve its effectiveness in predicting diabetes. The developed hyperparameter-tuned KNN model was tested on the diabetes dataset collected from the UCI machine learning data repository. The dataset contains 768 instances and 8 features. The study applied Min-max scaling to scale the data before fitting it to the KNN model. The result revealed KNN model performance improves when the hyperparameter is tuned. With hyperparameter tuning, the accuracy of KNN improves by 5.29% accuracy achieving 82.5% overall accuracy for predicting diabetes in the early stage. Therefore, the developed KNN model applied to clinical decision-making in predicting diabetes at an early stage. The early identification of diabetes could aid in early intervention, personalized treatment plans, or reducing healthcare costs reducing associated risks such as retinopathy, kidney disease, and cardiovascular disease.

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Clinical applications of artificial intelligence in diabetes management: A bibliometric analysis and comprehensive review

Daza,

Olivos-López,

Chumbirayco Pizarro

et al. 2024

Informatics in Medicine Unlocked

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A proposed approach for diabetes diagnosis using neuro-fuzzy technique

Cited by 6 publications

References 28 publications

Enhanced the prediction approach of diabetes using an autoencoder with regularization and deep neural network

Enhanced the prediction approach of diabetes using an autoencoder with regularization and deep neural network

Early Prediction of Gestational Diabetes with Parameter-Tuned K-Nearest Neighbor Classifier

Clinical applications of artificial intelligence in diabetes management: A bibliometric analysis and comprehensive review

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