Diagnosis of Diabetes using an Extreme Learning Machine Algorithm based Model

Shanthi, M.; Marimuthu, Ramalatha; Shivapriya, S. N.; Navaneethakrishnan, R.

doi:10.1109/icawst.2019.8923142

Cited by 8 publications

(6 citation statements)

References 11 publications

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“…Mother's pancreas is able to produce more insulin (about three times the normal amount) to overcome the effects of pregnancy hormones on blood sugar levels. However, if the pancreas cannot produce enough insulin to overcome the effect of the increase hormones during pregnancy, blood sugar levels will rise, causing GDM (Durnwald, 2015;Shanthi et al, 2019).…”

Section: Discussion Theme 1: Experiences Of Mothers In the Diagnosis mentioning

confidence: 99%

Life Experience of Pregnant Woman with Gestational Diabetes Mellitus in Maternal Role Attainment in Special Region of Yogyakarta

Wulaningsih

Hapsari

Pangastuti

et al. 2020

J. Ners

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Introduction: Gestational Diabetes Mellitus (GDM) is any abnormal carbohydrate intolerance that begins or is ﬁrst recognized during pregnancy. The presence of GDM has important implications for both the baby and the mother. Objective: This study explored life experience of pregnant woman with GDM in maternal role attainment. Methods: This was a phenomenological approach qualitative research. Participants were 12 mothers who gave birth to a maximum of 1 year with a history of GDM selected by purposive sampling technique with sampling criteria. Data analysis used the Colaizzi method (2011). Results: There were eight categories, e.g. the experience of the mother in GDM diagnosis; the influence GDM of the pregnancy; mother's experience in trying hard to keep her pregnancy; a variation of life experience of mothers during pregnancy, childbirth, and post-delivery; the perception of mother of her role being a mother, the factors that support the development and achievement of the maternal role attainment; the mother's experience in integrating the identity of her role as mother; gap and the desire of the mother with GDM in receiving health services. Life experience of GDM mothers in achieving maternal role attainment has a diversity. Discussion: Pregnant mothers with GDM experience various maternal role attainments. They cannot attain the role optimally.

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Section: Discussion Theme 1: Experiences Of Mothers In the Diagnosis mentioning

confidence: 99%

Life Experience of Pregnant Woman with Gestational Diabetes Mellitus in Maternal Role Attainment in Special Region of Yogyakarta

Wulaningsih

Hapsari

Pangastuti

et al. 2020

J. Ners

View full text Add to dashboard Cite

show abstract

“…Various studies [3][4][5][6][7][8][9][10][11][12][13][14][15] have been conducted to diagnose diabetes mellitus at an early stage using various machine learning.…”

Section: Related Workmentioning

confidence: 99%

“…The hybrid model optimizes the accuracy by lowering the mean square error. In [8] the usage of Extreme Learning Machine (ELM) method is proposed for diabetes diagnosis. Further, ELM accomplishes diabetes detection with less computational complexity.…”

Section: Related Workmentioning

confidence: 99%

Feature Transformation Through Stacked Autoencoder for Diabetes Classification

Vivekanandan¹,

Kumar²

2021

Lecture Notes in Electrical Engineering

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Diabetes Mellitus (Diabetes) refers to a chronic disability of the human body to process glucose in the bloodstream. Diabetes is widespread globally and is associated with high healthcare costs and can cause problems such as neuropathy and heart attacks along the line. Accurate diabetes prediction is essential for proper diagnosis and treatment thereafter. Therefore, the proposed work aims to develop a diabetes classification system. This work experiments feature learning through stacked auto encoder and feature fusion of low-level features as well as deep features extracted from the Pima Indians Dataset. The classification process is carried out using a shallow and deep neural network. The proposed feature transformation techniques are evaluated using relevant performance metrics. Using feature fusion, the metric scores are 92.71%, 92.37%, 93.44% and 96.80% for accuracy, sensitivity, specificity, and precision respectively. Henceforth, this study could be used to aid medical practitioners for efficient diabetes diagnosis and to provide treatment at an early stage.

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“…Diabetes mellitus of the circulatory system is chronic, persistent ailment triggered by excessive sugar levels. It can be classified into four types [ 4 – 6 ]. Type 1 diabetes is known as juvenile diabetes or insulin-dependent diabetes which occurs when the immune system of the body damages the insulin release cell; finally, it removes insulin production from the body [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Diabetes Mellitus Prediction and Severity Level Estimation Using OWDANN Algorithm

Annamalai¹,

Nedunchelian

2021

Computational Intelligence and Neuroscience

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Today, diabetes is one of the most prevalent, chronic, and deadly diseases in the world owing to some complications. If accurate early diagnosis is feasible, the risk factor and incidence of diabetes may be greatly decreased. Diabetes prediction is stable and reliable, since there are only minimal labelling evidence and outliers found in the datasets of diabetes. Numerous works coped with diabetes disease prediction and provided the solution. But the existing methods proffered low accuracy detection and consumed more training time. So, this paper proposed an OWDANN algorithm for diabetes mellitus disease prediction and severity level estimation. The proposed system mainly consists of two phases, namely, disease prediction and severity level estimation phase. In the disease prediction phase, the preprocessing is performed for the Pima dataset. Then, the features are extracted from the preprocessed data, and finally, the classification step is performed by using OWDANN. In the severity level estimation phase, the diabetes positive dataset is preprocessed first. Then, the features are extracted, and lastly, the severity level is predicted using GDHC. The extensive experimental results showed that the proposed system outperforms with 98.97% accuracy, 94.98% sensitivity, 95.62% specificity, 97.02% precision, 93.84% recall, 9404% f-measure, 0.094% FDR, and 0.023% FPR compared with the state-of-the-art methods.

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Diagnosis of Diabetes using an Extreme Learning Machine Algorithm based Model

Cited by 8 publications

References 11 publications

Life Experience of Pregnant Woman with Gestational Diabetes Mellitus in Maternal Role Attainment in Special Region of Yogyakarta

Life Experience of Pregnant Woman with Gestational Diabetes Mellitus in Maternal Role Attainment in Special Region of Yogyakarta

Feature Transformation Through Stacked Autoencoder for Diabetes Classification

Diabetes Mellitus Prediction and Severity Level Estimation Using OWDANN Algorithm

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