Machine learning techniques for medical diagnosis of diabetes using iris images

Samant, Piyush; Agarwal, Ravinder

doi:10.1016/j.cmpb.2018.01.004

Cited by 122 publications

(51 citation statements)

References 23 publications

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“…Sattlecker et al (2014) made use of machine learning method to recognize diagnostic Figure 1. The application fields of machine learning (Azqueta-Gavald on, 2017; Barzegar et al, 2017;Castiglioni et al, 2018;Chalouhi et al, 2017;Goh and Singh, 2015;Karim et al, 2018;Kim et al, 2017;Lázaro et al, 2017;Lee et al, 2017;Pham et al, 2016;Samant and Agarwal, 2018;Sattlecker et al, 2014;Shirzadi et al, 2017;Zeng et al,2018b;Zhang et al,2018a). spectral patterns in clinical practice, and emphasized the importance of the routine spectral data for the reanalysis process.…”

Section: Development Backgroundmentioning

confidence: 99%

Accuracy analyses and model comparison of machine learning adopted in building energy consumption prediction

Liu

et al. 2019

Energy Exploration & Exploitation

107

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It is of great significance to achieve the prediction of building energy consumption. However, machine learning, as a promising technique for many practical applications, was rarely utilized in this field. The most important reason is that the predictive structure with best performance is difficult to be determined. To fill the gap, this paper offers one in-depth review, which focuses on the accuracy analyses and model comparisons. Specifically, the accuracy analyses were conducted based on different types of buildings (e.g. residential building, commercial building, government building or educational building), different type of temporal granularity (e.g. sub-hourly, hourly, daily or annual), as well as input/output variables and historical data collections. Further, artificial neural network (ANN) and support vector machine (SVM), as the epidemic models, were compared in terms of their complexity of prediction processes, accuracies of results, the amounts of required historical data, the numbers of inputs, etc. Then the hybrid and single machine learning methods were outlined and compared in terms of their strengths and weaknesses. In addition, several vital defects and further research directions are presented from a multivariate perspective. We hope

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Section: Development Backgroundmentioning

confidence: 99%

Accuracy analyses and model comparison of machine learning adopted in building energy consumption prediction

Liu

et al. 2019

Energy Exploration & Exploitation

107

View full text Add to dashboard Cite

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“…The corresponding metric will give a measure of similarity between two iris models or template. It provides a range of values when comparing models of the same iris and another range of values when comparing different iris models [57]. Finally, a high confidence decision is made to identify whether the user is authenticated or not [58].…”

Section: Iris Recognitionmentioning

confidence: 99%

Efficient Biometric Iris Recognition Based on Iris Localization Approach

Al-Ani

2019

UHD J SCI TECH

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Biometric recognition is an emerging technology that has attracted more attention in recent years. This issue uses physiological and behavioral characteristics to identify an individual. The iris recognition approaches are among the most accurate biometric technologies with immense potential for use in global security applications. The purpose of this research is to implement an efficient approach to treat the diseased eye images in order to verify the second iris examination for the same person by inserting an improvement step to the iris recognition system. This step makes a correction of boundary around pupil and removes fraction regions. This approach demonstrates its ability to detect the inner limit of the iris. The obtained results shows that 90% success in the detection diseased eye images, which makes the iris recognition system more accurate and safe.

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“…A model was brought-in by Samant et al [20], which tries to compute the diagnostic validity of an old complementary and alternative medicine technique, iridology for diagnosis of type-2 diabetes through soft computing methods. Over a close group of total 338 subjects (180 diabetic and 158 nondiabetic) the analysis were done and then the infra-red images of both the eyes were captured simultaneously.…”

Section: Literature Reviewmentioning

confidence: 99%

Improved K-Means with Adaptive Divergence Weight Bat Algorithm (IKM-ADWBA) and Feature Selection for Type 2 Diabetes Mellitus Prediction

2019

IJRTE

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Increase in blood glucose (hyperglycaemia) leads to Diabetes Mellitus. There are two kinds of Diabetes mellitus: (Type 1 Diabetes Mellitus (T1DM) and (Diabetes Mellitus (T2DM), then former one is dependent on insulin and the latter one is independent of insulin. Various factors make it difficult to diagnose it. SO the author focuses at binging-in and analyzing the method for making a novel robust diagnosis system using data mining methods. Complete datasets is necessary for data mining techniques, but these techniques doesn’t give accurate results with missing values and all features. So, for prediction, Handling Missing value replacement and selection of important features are becomes a major issue. Hence, Adaptive Neuro Fuzzy Inference System (ANFIS) were proposed to acquire the missing value in dataset and to rectify the above mentioned issue. Then for an effective seed selection in Improved K-means algorithm, Enhanced Inertia Weight Binary Bat Algorithm (EIWBBA) is proposed, which results in high convergence speed. This research work proposed for feature selection with the help of Improved Distributed Kernel based Principal Component analysis (IDKPCA) with less time, after minimizing the entire feature space to the best features set. Then for classification of clustered samples, the author brought-in the Support Vector Machine (SVM). The experimental result confirms that the proposed algorithm gives the best classification accuracy rate when compared with other methods. From Pima Indians Diabetes, the data set has been considered and the experiment is done with the help of MATLAB for examining the Knowledge and the results were distinguished with other outcomes using appropriate toolkits.

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Machine learning techniques for medical diagnosis of diabetes using iris images

Cited by 122 publications

References 23 publications

Accuracy analyses and model comparison of machine learning adopted in building energy consumption prediction

Accuracy analyses and model comparison of machine learning adopted in building energy consumption prediction

Efficient Biometric Iris Recognition Based on Iris Localization Approach

Improved K-Means with Adaptive Divergence Weight Bat Algorithm (IKM-ADWBA) and Feature Selection for Type 2 Diabetes Mellitus Prediction

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