Diabetes detection using deep learning techniques with oversampling and feature augmentation

Garcí­a-Ordás, María Teresa; Benavides, Carmen; Bení­tez-Andrades, José Alberto; Aláiz‐Moretón, Héctor; García-Rodríguez, Isaías

doi:10.1016/j.cmpb.2021.105968

Cited by 66 publications

(30 citation statements)

References 30 publications

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“…We drew out some observations and findings from the review study as follows—majority of the included works focused on developing early, rapid or minimally invasive T2D prediction systems, with risk assessment considered to be implicitly carried out while prediction. A host of research works such as those presented in (Battineni et al 2019 ; Farran et al 2019 ; García-Ordás et al 2021 ; Kopitar et al 2020 ; Lai et al 2019 ; Lee and Kim 2016 ; Maniruzzaman et al 2018 ; Olivera et al 2017 ; Pei et al 2019 ; Zheng et al 2017 ; Zou et al 2018 ) have evaluated the predictive ability of various classifiers for T2D prediction with comparative analysis and some others have carried out the effectiveness of classifier predictions with different combinations of pre-processing (Maniruzzaman et al 2018 ; Wang et al 2019 ) and feature selection techniques (De Silva et al 2020 ; Roy et al 2021 ; Rubaiat et al ( 2018 ). Logistic Regression, a traditional statistical technique for binary and multivariate analysis has been considered in many of the included works owing to its simplicity and ability to model the interrelationships between dependent and independent variables.…”

Section: Discussionmentioning

confidence: 99%

Machine learning for diabetes clinical decision support: a review

Tuppad

Patil

2022

Adv. in Comp. Int.

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Type 2 diabetes has recently acquired the status of an epidemic silent killer, though it is non-communicable. There are two main reasons behind this perception of the disease. First, a gradual but exponential growth in the disease prevalence has been witnessed irrespective of age groups, geography or gender. Second, the disease dynamics are very complex in terms of multifactorial risks involved, initial asymptomatic period, different short-term and long-term complications posing serious health threat and related co-morbidities. Majority of its risk factors are lifestyle habits like physical inactivity, lack of exercise, high body mass index (BMI), poor diet, smoking except some inevitable ones like family history of diabetes, ethnic predisposition, ageing etc. Nowadays, machine learning (ML) is increasingly being applied for alleviation of diabetes health burden and many research works have been proposed in the literature to offer clinical decision support in different application areas as well. In this paper, we present a review of such efforts for the prevention and management of type 2 diabetes. Firstly, we present the medical gaps in diabetes knowledge base, guidelines and medical practice identified from relevant articles and highlight those that can be addressed by ML. Further, we review the ML research works in three different application areas namely—(1) risk assessment (statistical risk scores and ML-based risk models), (2) diagnosis (using non-invasive and invasive features), (3) prognosis (from normoglycemia/prior morbidity to incident diabetes and prognosis of incident diabetes to related complications). We discuss and summarize the shortcomings or gaps in the existing ML methodologies for diabetes to be addressed in future. This review provides the breadth of ML predictive modeling applications for diabetes while highlighting the medical and technological gaps as well as various aspects involved in ML-based diabetes clinical decision support.

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Section: Discussionmentioning

confidence: 99%

Machine learning for diabetes clinical decision support: a review

Tuppad

Patil

2022

Adv. in Comp. Int.

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“…Mujumdar and Vaidehi ( 16 ) suggested a diabetes prediction model for accurate diagnosis of diabetes that contains a few additional factors that are involved for diabetes in addition to standard indicators such as blood glucose, body mass index (BMI), age, insulin, and so on. Garca-Ordás et al ( 17 ) introduced an algorithm based on deep learning approaches to detect diabetes patients. Variational autoencoders (VAEs) can be used to add data and features, and a CNN can be used to classify the data.…”

Section: Related Workmentioning

confidence: 99%

A Decision Support System for Diagnosing Diabetes Using Deep Neural Network

Rabie

Alghazzawi

Asghar

et al. 2022

Front. Public Health

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Background and ObjectiveAccording to the WHO, diabetes mellitus is a long-term condition marked by high blood sugar levels. The consequences might be far-reaching. According to current increases in mortality, diabetes has risen to number 10 among the leading causes of mortality worldwide. When used to predict diabetes using unbalanced datasets from testing, machine learning (ML) classifiers and established approaches for encoding categorical data have exhibited a broad variety of surprising outcomes. Early studies also made use of an artificial neural network to extract features without obtaining a grasp of the sequence information.MethodsThis study offers a deep learning-based decision support system (DSS), utilizing bidirectional long/short-term memory (BiLSTM), to accurately predict diabetic illness from patient data. In order to predict diabetes, the BiLSTM hybrid model was used after balancing the data set.ResultsUnlike earlier studies, this proposed model's trial findings were promising, with an accuracy of 93.07%, 93% precision, 92% recall, and a 92% F1-score.ConclusionsUsing a BILSTM model for classification outperforms current approaches in the diabetes detection domain.

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“…Since the sample size grows, the oversampling technique takes longer to construct a model and can cause overfitting because it duplicates samples from a minor class. [23,24]. b) SMOTE: SMOTE is similar to random oversampling.…”

Section: ) Over-sampling Techniquementioning

confidence: 99%

Improving Imbalanced Data Classification in Auto Insurance by the Data Level Approaches

Hanafy¹,

Ming²

2021

IJACSA

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Predicting the frequency of insurance claims has become a significant challenge due to the imbalanced datasets since the number of occurring claims is usually significantly lower than the number of non-occurring claims. As a result, classification models tend to have a limited ability to predict the occurrence of claims. So, in this paper, we'll use various data level approaches to try to solve the imbalanced data problem in the insurance industry. We developed 32 machine learning models for predicting insurance claims occurrence {(undersampling, over-sampling, the combination of over-and undersampling (hybrid), and SMOTE)× (three Decision tree models, three boosting models, and two bagging models) = 32}, and we compared the models' accuracies, sensitivities, and specificities to comprehend the prediction performance of the built models. The dataset contains 81628 claims, each of which is a car insurance claim. There were 5714 claims that occurred and 75914 claims that didn't occur. According to the findings, the AdaBoost classifier with oversampling and the hybrid method had the most accurate predictions, with a sensitivity of 92.94%, a specificity of 99.82%, and an accuracy of 99.4%. And with a sensitivity of 92.48%, a specificity of 99.63%, and an accuracy of 99.1%, respectively. This paper confirmed that When analyzing imbalanced data, the AdaBoost classifier, whether using oversampling or the hybrid process, could generate more accurate models than other boosting models, Decision tree models, and bagging models.

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Diabetes detection using deep learning techniques with oversampling and feature augmentation

Cited by 66 publications

References 30 publications

Machine learning for diabetes clinical decision support: a review

Machine learning for diabetes clinical decision support: a review

A Decision Support System for Diagnosing Diabetes Using Deep Neural Network

Improving Imbalanced Data Classification in Auto Insurance by the Data Level Approaches

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