Functional link convolutional neural network for the classification of diabetes mellitus,International Journal for Numerical Methods in Biomedical Engineering

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Functional link convolutional neural network for the classification of diabetes mellitus
International Journal for Numerical Methods in Biomedical Engineering ( IF 2.2 ) Pub Date : 2021-05-08 , DOI: 10.1002/cnm.3496
Sunil Kumar Jangir ₁ , Nakul Joshi ₂ , Manish Kumar ₃ , Dilip Kumar Choubey ₄ , Shatakshi Singh ₁ , Madhushi Verma ₅

Affiliation

Diabetes is a faction of metabolic ailments distinguished by hyperglycemia which is the consequence of a defect, in the action of insulin, insulin secretion, or both and producing various abnormalities in the human body. In recent years, the utilization of intelligent systems has been expanded in disease classification and numerous researches have been proposed. In this research article, a variant of Convolutional Neural Network (CNN) that is, Functional Link Convolutional Neural Network (FLCNN) is proposed for the diabetes classification. The main goal of this article is to find the potential of a computationally less complex deep learning network like FLCNN and applied the proposed technique on a real dataset of diabetes for classification. This article also presents the comparative studies where various other machine learning techniques are implemented and outcomes are compared with the proposed FLCNN network. The performance of each classification techniques have been evaluated based on standard measures and also validated with a non-parametric statistical test such as Friedman. Data for modeling diabetes classification is collected from Bombay Medical Hall, Upper Bazar, Ranchi, India. Accuracy achieve by the proposed classifier is more than 90% which is closer to the other state-of-the-art implemented classifiers.

中文翻译：

用于糖尿病分类的功能链接卷积神经网络

糖尿病是一组以高血糖为特征的代谢疾病，高血糖是胰岛素作用、胰岛素分泌或两者都有缺陷的结果，并在人体中产生各种异常。近年来，智能系统在疾病分类中的应用得到了扩展，并提出了许多研究。在这篇研究文章中，提出了一种卷积神经网络 (CNN) 的变体，即功能链接卷积神经网络 (FLCNN)，用于糖尿病分类。本文的主要目标是发现像 FLCNN 这样的计算不太复杂的深度学习网络的潜力，并将所提出的技术应用于糖尿病的真实数据集进行分类。本文还介绍了实施各种其他机器学习技术并将结果与建议的 FLCNN 网络进行比较的比较研究。每种分类技术的性能都基于标准度量进行了评估，并且还通过非参数统计测试（如弗里德曼）进行了验证。用于模拟糖尿病分类的数据收集自印度兰契上巴扎尔的 Bombay Medical Hall。所提出的分类器实现的准确度超过 90%，这更接近于其他最先进的实现分类器。用于模拟糖尿病分类的数据收集自印度兰契上巴扎尔的 Bombay Medical Hall。所提出的分类器实现的准确度超过 90%，这更接近于其他最先进的实现分类器。用于模拟糖尿病分类的数据收集自印度兰契上巴扎尔的 Bombay Medical Hall。所提出的分类器实现的准确度超过 90%，这更接近于其他最先进的实现分类器。

更新日期：2021-05-08

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