Prediction of diabetes mellitus (NIDDM)

Hyperglycemia is a common manifestation in the course of severe disease and is the result of acute metabolic and hormonal changes associated with various factors such as trauma, stress, surgery, or infection. Numerous studies demonstrate the association of adverse clinical events with stress hyperglycemia. This article briefly describes the pathophysiological mechanisms which lead to hyperglycemia under stressful circumstances particularly in the pediatric and adolescent population. The importance of prevention of hyperglycemia, especially for children, is emphasized and the existing models for the prediction of diabetes are presented. The available studies on the association between stress hyperglycemia and progress to type 1 diabetes mellitus are presented, implying a possible role for stress hyperglycemia as part of a broader prognostic model for the prediction and prevention of overt disease in susceptible patients.

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Comparative Analysis of Data Preparation Methods Employed in Prediction of Diabetes Mellitus Diagnosis using eICU-CRD Dataset.

10.1109/smartgencon51891.2021.9645891 ◽

2021 ◽

Author(s):

Sai Lakshmi Nikhita Sagi ◽

Mamatha Narsapuram ◽

Pravallika Nakarikanti ◽

Sahithi Sane ◽

Sai Sudha Vadisina ◽

...

Keyword(s):

Diabetes Mellitus ◽

Comparative Analysis ◽

Data Preparation ◽

Preparation Methods ◽

Diabetes Mellitus Diagnosis ◽

Prediction Of Diabetes

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Predictive Analysis of Diabetes Mellitus Using Machine Learning Techniques

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2020.9207 ◽

2020 ◽

Vol 17 (8) ◽

pp. 3449-3452

Author(s):

M. S. Roobini ◽

Y. Sai Satwick ◽

A. Anil Kumar Reddy ◽

M. Lakshmi ◽

D. Deepa ◽

...

Keyword(s):

Diabetes Mellitus ◽

Machine Learning ◽

Early Stage ◽

The Body ◽

Machine Learning Techniques ◽

Support Vector ◽

Pima Indians ◽

Classification Techniques ◽

Learning Techniques ◽

Prediction Of Diabetes

In today’s world diabetes is the major health challenges in India. It is a group of a syndrome that results in too much sugar in the blood. It is a protracted condition that affects the way the body mechanizes the blood sugar. Prevention and prediction of diabetes mellitus is increasingly gaining interest in medical sciences. The aim is how to predict at an early stage of diabetes using different machine learning techniques. In this paper basically, we use well-known classification that are Decision tree, K-Nearest Neighbors, Support Vector Machine, and Random forest. These classification techniques used with Pima Indians diabetes dataset. Therefore, we predict diabetes at different stage and analyze the performance of different classification techniques. We Also proposed a conceptual model for the prediction of diabetes mellitus using different machine learning techniques. In this paper we also compare the accuracy of the different machine learning techniques to finding the diabetes mellitus at early stage.

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