Determining diabetes using iris recognition system

Bansal, Atul; Agarwal, Ravinder; Sharma, Rajiv Kumar

doi:10.1007/s13410-015-0296-1

Cited by 37 publications

(9 citation statements)

References 22 publications

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“…The study used eye images features of 80 patients for diabetes diagnosis. The rate of accuracy given by the study was 87.50% 89 . P Samant et al, (2017), diagnose diabetes by analysing iris data of 200 patients using several data mining algorithms such as binary tree, random forest, SVM, neural networks.…”

Section: Rate Of Accuracies Achieved By Analysingmentioning

confidence: 72%

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2018

IJPSR

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Diabetes is one of the deadliest and non-contagious diseases that can adversely affect several parts of human body. Early prognosis of diabetes can inkling the grievous complications and help to save human life. Several researchers have used different data mining (Iterative Dichotomiser 3, Random Forest, Support Vector Machine, k-Nearest Neighbour, C4.5) and soft computing (Genetic Algorithm, Ant Colony Optimization, Particle Swarm optimization, Artificial Bee Colony) techniques to prospect diabetes in human beings. In last 10 years, C4.5 was the most preferred choice for mining diabetic patients. Likewise, in soft computing, maximum number of researchers have used genetic algorithm. Furthermore, the usage of pre-processing techniques is significantly increasing in diabetes diagnosis. It is also observed that rate of accuracy achieved in diagnosing diabetes using traditional data mining lies in 68.5%-95.3%. Likewise, the range for soft computing and their hybridized use lies in 74%-100%. In addition, rate of accuracy achieved using GA based hybridized approach is better than the accuracy obtained using PSO as well as ABC. Most of the researchers have used textual and numeric data for diabetes diagnosis. Few researchers have used images for the same. However, no significant research is found where diabetes has been diagnosed using audio or sound. Moreover, the diagnostic results obtained using image based data are not as good as obtained using textual or discrete data. Therefore, an attention is still obligatory to develop smart diabetes diagnostic system that can effectively work on different types of data like text, images as well as sound. INTRODUCTION: Diabetes is a persistent metabolic and pancreas affected human disorder. The devastating rise in obesity and sedentary life style has made it as a universal epidemic 1, 2. Numbers of individuals are getting affected by different types of diabetes. Human body changes most of the food into glucose and it is the primary source of energy in the body. Pancreas assists in digestion and releases a hormone called insulin that transforms glucose into body cells.

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Section: Rate Of Accuracies Achieved By Analysingmentioning

confidence: 72%

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2018

IJPSR

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“…In this approach patient can receive the correct treatments based their iris tissues information. This approach is mainly depending on the three important procedures: More insights about the usage of SVM Studies were related to chronic renal failure and the signal in the kidney region A. Bansal, R. Agarwal, R.K. Sharma [23] Using MATLAB for programming for various calculations in pre-processing the image MATLAB, the programming software for numerical and matrix calculation, signal, and graphic processing D. Grayscale adjusted image In the normalization process, the image resolution is decrease in order to complete the operation. The unwanted part of the image that is generated is removed and quality of the image is increased to meet the specified resolution.…”

Section: Iris Design Based On the Clinical Recognition Approachmentioning

confidence: 99%

An efficient machine learning approach to nephrology through iris recognition

Divya¹,

Gururaj²,

R³

et al. 2021

Discov Artif Intell

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Iridology is a technique in science used to analyze color, patterns, and various other properties of the iris to assess an individual's general health. Few regions in the iris are connected by nerves coming from different organs of body, this shows some special unique qualities which is advantageous along with which assist in psychological condition, particular organ conditions and construction of the body. The structural and designed patterns present on specific part of iris represent the level of intensity of disorder caused by the organs. This method of approach can be employed as reasonable and logical guidelines for the detection and identification of disorders. Therefore, after scanning the image of iris advance study of disorder can be carried out for detecting the condition of organ. Initially by the service of an adaptive histogram, the image of eye should be separated from part of the image captured. Next the images of iris are classified and recognized using machine learning algorithm Support Vector machine or Support Vector Networks. The features are extracted from images of iris using white Gaussian filters which are then used as a feature descriptor. These descriptors count the occurrences of gradient orientation and magnitude in localized portions of an image. Then convert the image of iris to a gray scaled image, final image is standardized. Next is to convert it into rectangular shape and then assembling the HMM images of eyes related to the kidney. The final level is to diagnose the edge of image of iris HMM. By analysing end results, condition of the organ can be diagnosed and results can be obtained from the iris recognition system.

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“…The diseases that are observed are disorders of the stomach. Bansal et al [3] detected a body disorder in iridology using simple image processing in his evidence. The result showed that iridology is an alternative technique that can be used by looking at the iris to find out the status of organs with a very effective introduction algorithm combined with iridology.…”

Section: Introductionmentioning

confidence: 95%

Upper Stomach Disorder Detection System using Backpropagation Artificial Neural Network

Hidayanti¹

2020

IJETER

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The study aims to make simple detection of upper stomach disorders of the iris image based on the chart to iridology, making an upper stomach disorder detection system using backpropagation artificial neural network method and determining the accuracy of the system. Backpropagation artificial neural networks is a type of neural network that trains the network to get a balance between the network's ability to recognize patterns used during training as well as networking capabilities to provide a correct response to similar input patterns but not identical with patterns during training. Iridology is the science of analyzing the subtle structures of the iris. This research was conducted from the shooting stage of the eye image using the camera, data obtained as 40 iris images. Detection of disorders using chart to iridology from the iris imagery data of 20 pairs of eyes consisting of left and right. This results of 10 pairs of eye image showed that upper stomach disorder and 10 pairs of eye image showed no upper stomach disorder.

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Determining diabetes using iris recognition system

Cited by 37 publications

References 22 publications

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An efficient machine learning approach to nephrology through iris recognition

Upper Stomach Disorder Detection System using Backpropagation Artificial Neural Network

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