Assessment of chronic kidney disease using skin texture as a key parameter: for South Indian population

Udhayarasu, Madhanlal; Ramakrishnan, Kalpana; Periasamy, Sivakumar

doi:10.1049/htl.2016.0098

Cited by 8 publications

(5 citation statements)

References 25 publications

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“…This approach is specifically designed based on IgAN attributes leading to kidney failure. Udhayarasu et al [14] determined the kidney function noninvasively by deriving a skin parameter from skin images to calculate eGFR without creatinine. But this method also relied on the Modification of Diet in Renal Disease (MDRD) equation.…”

Section: Related Workmentioning

confidence: 99%

Selecting Dominant Features for the Prediction of Early-Stage Chronic Kidney Disease

Arumugam¹,

Priya²

2022

Intelligent Automation &Amp; Soft Computing

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Nowadays, Chronic Kidney Disease (CKD) is one of the vigorous public health diseases. Hence, early detection of the disease may reduce the severity of its consequences. Besides, medical databases of any disease diagnosis may be collected from the blood test, urine test, and patient history. Nevertheless, medical information retrieved from various sources is diverse. Therefore, it is unadaptable to evaluate numerical and nominal features using the same feature selection algorithm, which may lead to fallacious analysis. Applying machine learning techniques over the medical database is a common way to help feature identification for CKD prediction. In this paper, a novel Mixed Data Feature Selection (MDFS) model is proposed to select and filter preeminent features from the medical dataset for earlier CKD prediction, where CKD clinical data with 12 numerical and 12 nominal features are fed to the MDFS model. For each feature in the mixed dataset, the model applies feature selection methods according to the data type of the feature. Point Biserial correlation and a Chi-square filter are applied to filter the numerical features and nominal features, respectively. Meanwhile, an SVM algorithm is employed to evaluate and select the best feature subset. In our experimental results, the proposed MDFS model performs superior to existing works in terms of accuracy and the number of reduced features. The identified feature subset is also demonstrated to preserve its original properties without discretization during feature selection.

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Section: Related Workmentioning

confidence: 99%

Selecting Dominant Features for the Prediction of Early-Stage Chronic Kidney Disease

Arumugam¹,

Priya²

2022

Intelligent Automation &Amp; Soft Computing

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“…B. „Community“ im Sinne von „in einer Gemeinde“ [ 74 ] oder „PAR“ nicht als Abkürzung für Participatory Action Research nutzen [ 75 ]). Die Schwierigkeiten, die sich zudem aufgrund der hohen Methodenanzahl für eine vollständige Rücküberprüfung ergeben haben, zeigen, dass die Debatte mit diesem Beitrag erst einen Startpunkt markieren kann.…”

Section: Diskussionunclassified

Categorizing methods used in participatory research

Kasberg

Müller

Markert

et al. 2020

Bundesgesundheitsbl

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Zusammenfassung Hintergrund In der Forschung gewinnen partizipative Ansätze an Bedeutung. Der Einsatz partizipativer Methoden erfolgt meist ohne eine methodologische Auseinandersetzung oder den Bezug auf eine gemeinsame Systematik. Ziel der Arbeit Auf der Grundlage eines Scoping-Reviews wird eine Systematisierung partizipativer Methoden vorgeschlagen. Damit wird ein Beitrag zum Diskurs über partizipative Methoden geleistet, die gesundheitliche Chancengleichheit fördern. Material und Methoden Nach einer Literaturrecherche wurden 44 Treffer eingeschlossen. Die Suche sowohl nach deutsch- als auch englischsprachiger Literatur basierte auf diversen Datenbanken (PubMed, PsycInfo, SocIndex, Livivo, Cochrane, Fachportal Pädagogik), einer Handsuche und einer Suche nach dem Schneeballprinzip. Ergebnisse und Diskussion Die Systematisierung kombiniert phasen- und formatbasierte Logiken. Als zentrale Prinzipien in den Definitionen einer partizipativen Methodologie und von partizipativen Methoden werden die Mehrdimensionalität und Phasenintegration berücksichtigt. Daraus abgeleitet werden die Hauptunterscheidung von prozessgestaltenden und forschenden Methoden. Schwerpunkte zeigen sich bei den Erhebungsmethoden und Reflexionsverfahren. Lücken im deutschsprachigen Raum sind bei Methoden gemeinsamer Entscheidungsfindungen, der Auswertung und Verwendung erkennbar. Fazit Die Ergebnisse helfen bei der Einordnung verschiedener Ansätze und ihres Grades an Partizipation. Für Forschung und Lehre wird die Aufmerksamkeit auf das Spektrum forschender und prozessgestaltender partizipativer Methoden gelenkt, die es einzusetzen, zu beschreiben und zu vermitteln gilt.

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“…In recent times, this has been explored as an attempt to find KD from EDA, especially diabetic kidney disease (DKD). 15,16…”

Section: Introductionmentioning

confidence: 99%

“…In recent times, this has been explored as an attempt to find KD from EDA, especially diabetic kidney disease (DKD). 15,16 From the above discussion, it is clear that though EDA helps in identifying diabetes and related diseases. Since SGs are controlled by autonomic nervous system and its functions are similar to kidney, these glands are explored to distinguish diabetic neuropathy and diabetic nephropathy from controls.…”

Section: Introductionmentioning

confidence: 99%

Electrodermal signal analysis using continuous wavelet transform as a tool for quantification of sweat gland activity in diabetic kidney disease

Singaram¹,

Ramakrishnan²,

Soundararajan

2023

Proc Inst Mech Eng H

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Sympathetic innervation of the sweat gland (SG) manifests itself electrically as electrodermal activity (EDA), which can be utilized to measure sudomotor function. Since SG exhibits similarities in structure and function with kidneys, quantification of SG activity is attempted through EDA signals. A methodology is developed with electrical stimulation, sampling frequency and signal processing algorithm. One hundred twenty volunteers participated in this study belonging to controls, diabetes, diabetic nephropathy, and diabetic neuropathy. The magnitude and time duration of stimuli is arrived by trial and error in such a way it does not influence controls but triggers SG activity in other Groups. This methodology leads to a distinct EDA signal pattern with changes in frequency and amplitude. The continuous wavelet transform depicts a scalogram to retrieve this information. Further, to distinguish between Groups, time average spectrums are plotted and mean relative energy (MRE) is computed. Results demonstrate high energy value in controls, and it gradually decreases in other Groups indicating a decline in SG activity on diabetes prognosis. The correlation for the acquired results was determined to be 0.99 when compared to the standard lab procedure. Furthermore, Cohen’s d value, which is less than 0.25 for all Groups indicating the minimal effect size. Hence the obtained result is validated and statistically analyzed for individual variations. Thus this has the potential to get transformed into a device and could prevent diabetic kidney disease.

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Assessment of chronic kidney disease using skin texture as a key parameter: for South Indian population

Cited by 8 publications

References 25 publications

Selecting Dominant Features for the Prediction of Early-Stage Chronic Kidney Disease

Selecting Dominant Features for the Prediction of Early-Stage Chronic Kidney Disease

Categorizing methods used in participatory research

Electrodermal signal analysis using continuous wavelet transform as a tool for quantification of sweat gland activity in diabetic kidney disease

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