Analysis of computational techniques for diabetes diagnosis using the combination of iris-based features and physiological parameters

Samant, Piyush; Agarwal, Ravinder

doi:10.1007/s00521-019-04551-9

Cited by 16 publications

(8 citation statements)

References 35 publications

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“…In this paper, they use Gray Level Co-Occurrence Matrix (GLCM) feature extraction and Bayesian regularization (BR) classification to diagnose either the subject is suffering from the disease or not. Arcus Senilis [13], [22][23][24][25][26][27] Autonomic Nerve [28] Brain [29] Heart [12], [16], [30][31] Kidney [32] Liver [33] Lung [4], [15], [34] Pancreas [3], [7], [8], [14], [35][36][37] Stomach [38] Iris Recognition System ROI and Accuracy…”

Section: Discussionmentioning

confidence: 99%

Retracted: A Review of Iris Recognition System ROI and Accuracy

Halim

Emanuel

2020

2020 International Conference on Smart Technology and Applications (ICoSTA)

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Section: Discussionmentioning

confidence: 99%

Retracted: A Review of Iris Recognition System ROI and Accuracy

Halim

Emanuel

2020

2020 International Conference on Smart Technology and Applications (ICoSTA)

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“…Three ensemble techniques, AdaBoost, Bagging, and K-NN, are also used with Principal Component Analysis (PCA) to enhance classification performance. The highest accuracy of 95.81% is obtained with the most significant features ( 53 ). A convolution neural network (CNN) is a feed-forward and feature extractor Neural Network.…”

Section: Literature Reviewmentioning

confidence: 98%

An Augmented Artificial Intelligence Approach for Chronic Diseases Prediction

Rashid

Batool

Kim

et al. 2022

Front. Public Health

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Chronic diseases are increasing in prevalence and mortality worldwide. Early diagnosis has therefore become an important research area to enhance patient survival rates. Several research studies have reported classification approaches for specific disease prediction. In this paper, we propose a novel augmented artificial intelligence approach using an artificial neural network (ANN) with particle swarm optimization (PSO) to predict five prevalent chronic diseases including breast cancer, diabetes, heart attack, hepatitis, and kidney disease. Seven classification algorithms are compared to evaluate the proposed model's prediction performance. The ANN prediction model constructed with a PSO based feature extraction approach outperforms other state-of-the-art classification approaches when evaluated with accuracy. Our proposed approach gave the highest accuracy of 99.67%, with the PSO. However, the classification model's performance is found to depend on the attributes of data used for classification. Our results are compared with various chronic disease datasets and shown to outperform other benchmark approaches. In addition, our optimized ANN processing is shown to require less time compared to random forest (RF), deep learning and support vector machine (SVM) based methods. Our study could play a role for early diagnosis of chronic diseases in hospitals, including through development of online diagnosis systems.

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“…-Medicina [Rao et al, 2006] [Mazurowski et al, 2008] [Mena and González, 2009] [Freitas, 2011] [Nahar et al, 2013] [Samant and Agarwal, 2019; -Bio-Informática [Radivojac et al, 2004] [Batuwita and Palade, 2009] [Yu et al, 2013] [Triguero et al, 2015;…”

Section: Capítulo 2 Problemas De Clasificación Desequilibradamentioning

confidence: 99%

On the design of Bayesian principled algorithms for imbalanced classification

Benítez-Buenache

Álvarez-Pérez

Figueiras-Vidal

2021

Knowledge-Based Systems

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Singular Problems are those whose characteristics compromise the correct operation of conventional discriminative machines, obtaining unsatisfactory results. Among them, imbalanced classification problems stand out, those in which there are large differences in the class populations or/and the cost policy penalizes to a greater extent the choice of certain classes, biasing the machine output in favor of the predominant classes. Therefore, the application of specific methods that compensate the imbalance is required, allowing the detection of the minority classes.Particularly for the binary case, a state-of-the-art survey of the existing rebalancing methods is carried out. Most of the proposed techniques are purely empirical, without a complete analysis of the statistical implications of their application.Although their use may provide good results under certain conditions, any change in these conditions may lead to a degradation of their performance. Therefore, a principled methodology based on Bayesian statistical theory is presented with the aim of constructing robust solutions. This methodology is based on the likelihood ratio invariance principle, for which two sufficient and necessary conditions are established: the use of Bregman divergences as a surrogate cost and statistically neutral rebalancing methods. In addition, principled two-step classification procedures are proposed and a rebalanced design process based on the combination of methods is described in detail. Several experiments support the methodology, studying its effects and limitations in real problems under different circumstances: larger or smaller number of available samples and presence of noise.Finally, the SMOTE algorithm, one of the most common rebalancing methods, is studied in more depth. Due to the filiform generation of samples −by means of the nearest neighbors−, SMOTE presents difficulties with high dimensionality problems.Therefore, an alternative, VoluSMOTE, is proposed to correct or mitigate such effects by volumetric generation."La perfeccción es una pulida corrección de errores"

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Analysis of computational techniques for diabetes diagnosis using the combination of iris-based features and physiological parameters

Cited by 16 publications

References 35 publications

Retracted: A Review of Iris Recognition System ROI and Accuracy

Retracted: A Review of Iris Recognition System ROI and Accuracy

An Augmented Artificial Intelligence Approach for Chronic Diseases Prediction

On the design of Bayesian principled algorithms for imbalanced classification

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