UGC Approved Journal no 63975(19)

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Published in:

Volume 6 Issue 5
May-2019
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIRDA06025


Registration ID:
221474

Page Number

144-151

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Title

Supervised Learning Method n Neural Network Algorithm for the Analysis of Diabetic Mellitus and its Comparative Analysis

Abstract

Diabetes is the key critical issue needs to be concerned for various problems in our body. Increase in glucose and fructose content in our body results diabetes mellitus. When a body generates higher insulin level than the required, results increased urination and excessive thirstiness which in turn results kidney failure and other cardio related issues. Many research agencies invested their funds on defining the predictive methodology and finding the root cause those results in mellitus. Mellitus results highest mortality rate compared to any other disease reported by the health organizations across the globe. In this, the predictive methodologies, various classification techniques are discussed and the results are analyzed. The classification methodology could be on medications, Food Habits, Personal behaviours, Age factors and so on. The datasets are processed and analyzed with the Neural Network algorithms and the results are compared with one another. The datasets are taken from the National Family Health Survey results published during the period of 2016-2017. The result implies that men between ages 15-49 among 1 billion people have reported with diabetes mellitus. Diagnose and forecast on this disease is done by recognizing the pattern formation and grouping the similar structures. Various algorithmic techniques like M-Layer Perceptron, Nearest Neighbour, Vector machines, Data regressions, Binary Regression and their accuracy of forecast, speed and sensitivity is calculated, analyzed and compared to define the accurate prediction methodology over a short span of time. The Forecast methodologies are focussed to provide solutions to avoid the intensive care system provided proper medications with a long duration when it is been predicted to be a risk factor. A statistical method of analyzing is performed for the comparative analysis. The learning and training methodologies are discussed in this system. Accuracy, Specificity, sensitivity is the key parameters to define the best forecast methodology. Classification on Association, Regression techniques and Neural Algorithmic techniques are analyzed and compared to refine the best predictive forecast methodology by processing 30 Samples across the states of India with focus on determining the type of mellitus along with the accuracy on definition. The forecast data utilized to define the type of mellitus and the prediction on critical measures over a period of time.

Key Words

Mellitus, Neural Algorithms, Mellitus Classification, M-Layer Perceptron, Regression techniques, Nearest Neighbour, Learning techniques, MATLAB.

Cite This Article

"Supervised Learning Method n Neural Network Algorithm for the Analysis of Diabetic Mellitus and its Comparative Analysis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 5, page no.144-151, May 2019, Available :http://www.jetir.org/papers/JETIRDA06025.pdf

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2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"Supervised Learning Method n Neural Network Algorithm for the Analysis of Diabetic Mellitus and its Comparative Analysis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 5, page no. pp144-151, May 2019, Available at : http://www.jetir.org/papers/JETIRDA06025.pdf

Publication Details

Published Paper ID: JETIRDA06025
Registration ID: 221474
Published In: Volume 6 | Issue 5 | Year May-2019
DOI (Digital Object Identifier):
Page No: 144-151
Country: -, -, - .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


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