Leveraging Pima Dataset to Diabetes Prediction: Case Study of Deep Neural Network

Hounguè, Pélagie; Bigirimana, Annie Ghylaine

doi:10.4236/jcc.2022.1011002

Cited by 4 publications

(3 citation statements)

References 21 publications

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“…• K-fold Cross-Validation involves splitting the data into k subsets. One of the k subsets are used as the validation set, whereas the other k-1 subsets is used as the training set 23 .…”

Section: Output Layermentioning

confidence: 99%

Pediatric diabetes prediction using deep learning

El-Bashbishy,

El-Bakry

2024

Sci Rep

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This study proposed a novel technique for early diabetes prediction with high accuracy. Recently, Deep Learning (DL) has been proven to be expeditious in the diagnosis of diabetes. The supported model is constructed by implementing ten hidden layers and a multitude of epochs using the Deep Neural Network (DNN)-based multi-layer perceptron (MLP) algorithm. We proceeded to meticulously fine-tune the hyperparameters within the fully automated DL architecture to optimize data preprocessing, prediction, and classification using a novel dataset of Mansoura University Children's Hospital Diabetes (MUCHD), which allowed for a comprehensive evaluation of the system’s performance. The system was validated and tested using a sample of 548 patients, each with 18 significant features. Various validation metrics were employed to ensure the reliability of the results using cross-validation approaches with various statistical measures of accuracy, F-score, precision, sensitivity, specificity, and Dice similarity coefficient. The high performance of the proposed system can help clinicians accurately diagnose diabetes, with a remarkable accuracy rate of 99.8%. According to our analysis, implementing this method results in a noteworthy increase of 0.39% in the overall system performance compared to the current state-of-the-art methods. Therefore, we recommend using this method to predict diabetes.

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“…• K-fold Cross-Validation involves splitting the data into k subsets. One of the k subsets are used as the validation set, whereas the other k-1 subsets is used as the training set 23 .…”

Section: Output Layermentioning

confidence: 99%

Pediatric diabetes prediction using deep learning

El-Bashbishy,

El-Bakry

2024

Sci Rep

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show abstract

“…In the Validation process, we run the suggested model on various subsets of both training and validation datasets, then we get model quality measures [23]. This step can be further categorized into two techniques: exhaustive and non-exhaustive cross-validation.…”

Section: Train/testmentioning

confidence: 99%

“…Nh K-fold Cross-Validation involves splitting the data into k subsets. One of the k subsets is used as the validation set, while the other k-1 subsets are used as the training set [22][23].…”

Section: Train/testmentioning

confidence: 99%

Pediatric Diabetes Prediction Using Deep Learning

El-Bashbishy,

El-Bakry

2023

Preprint

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The present study proposes a novel technique for the early prediction of diabetes with the utmost accuracy. Recently, the contemporary methodologies of artificial intelligence and in particular Deep Learning (DL), have proven to be expeditious in the diagnosis of diabetes. The model that is supported has been constructed with the implementation of two hidden layers and a multitude of epochs of Deep Learning Neural Network (DLNN) utilizing the Multi-Layer Perceptron (MLP) technique. We proceeded to meticulously adjust the hyperparameters within the fully automated DLNN architecture, with the aim of optimizing data pre-processing, classification and prediction. This was accomplished by a novel dataset of Mansoura University Children's Hospital Diabetes (MUCHD), which allowed for a more comprehensive evaluation of the system’s performance. The system is validated and tested on a sample of 548 patients, each exhibiting 18 significant features. Various validation metrics were employed to ensure the accuracy and reliability of the results like K-folds, leave-one-subject-out and cross-validation approaches with various statistical measures of accuracy, f-score, precision, sensitivity, specificity and dice similarity coefficient. The high-performance level of the proposed system can help clinicians to accurately diagnose health and different diabetes grades with a remarkable accuracy rate of 99.8%. According to our analysis, the implementation of this method results in a noteworthy increase of 4.15% in overall system performance when compared to the current state-of-the-art. As such, we highly recommend the utilization of this method as a promising tool for forecasting diabetes.

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Demystify Artificial Intelligence

Prentice

2023

Leveraging Emotional and Artificial Intelligence for Organisational Performance

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Leveraging Pima Dataset to Diabetes Prediction: Case Study of Deep Neural Network

Cited by 4 publications

References 21 publications

Pediatric diabetes prediction using deep learning

Pediatric diabetes prediction using deep learning

Pediatric Diabetes Prediction Using Deep Learning

Demystify Artificial Intelligence

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