Sonia J Jeba, Jayachandran Prassanna, Md Abdul Quadir, Mohan Senthilkumar, Sivaraman Arun Kumar, Tee Kong Fah
Department of Data Science and Business Systems, School of Computing, SRM Institute of Science and Technology, College of Engineering and Technology, Kattankulathur, Chennai 603203, India.
School of Computer Science and Engineering, Vellore Institute of Technology, Chennai 600127, India.
Diagnostics (Basel). 2023 Feb 14;13(4):723. doi: 10.3390/diagnostics13040723.
Over the past few decades, the prevalence of chronic illnesses in humans associated with high blood sugar has dramatically increased. Such a disease is referred to medically as diabetes mellitus. Diabetes mellitus can be categorized into three types, namely types 1, 2, and 3. When beta cells do not secrete enough insulin, type 1 diabetes develops. When beta cells create insulin, but the body is unable to use it, type 2 diabetes results. The last category is called gestational diabetes or type 3. This happens during the trimesters of pregnancy in women. Gestational diabetes, however, disappears automatically after childbirth or may continue to develop into type 2 diabetes. To improve their treatment strategies and facilitate healthcare, an automated information system to diagnose diabetes mellitus is required. In this context, this paper presents a novel system of classification of the three types of diabetes mellitus using a multi-layer neural network no-prop algorithm. The algorithm uses two major phases in the information system: the training phase and the testing phase. In each phase, the relevant attributes are identified using the attribute-selection process, and the neural network is trained individually in a multi-layer manner, starting with normal and type 1 diabetes, then normal and type 2 diabetes, and finally healthy and gestational diabetes. Classification is made more effective by the architecture of the multi-layer neural network. To provide experimental analysis and performances of diabetes diagnoses in terms of sensitivity, specificity, and accuracy, a confusion matrix is developed. The maximum specificity and sensitivity values of 0.95 and 0.97 are attained by this suggested multi-layer neural network. With an accuracy score of 97% for the categorization of diabetes mellitus, this proposed model outperforms other models, demonstrating that it is a workable and efficient approach.
在过去几十年中，与高血糖相关的人类慢性疾病患病率急剧上升。这种疾病在医学上被称为糖尿病。糖尿病可分为三种类型，即1型、2型和3型。当β细胞分泌的胰岛素不足时，就会发展为1型糖尿病。当β细胞产生胰岛素，但身体无法利用它时，就会导致2型糖尿病。最后一类称为妊娠期糖尿病或3型糖尿病。这种情况发生在女性怀孕的三个月期间。然而，妊娠期糖尿病在分娩后会自动消失，或者可能继续发展为2型糖尿病。为了改进治疗策略并促进医疗保健，需要一个自动诊断糖尿病的信息系统。在此背景下，本文提出了一种使用多层神经网络无支撑算法对三种类型糖尿病进行分类的新型系统。该算法在信息系统中使用两个主要阶段：训练阶段和测试阶段。在每个阶段，通过属性选择过程识别相关属性，并以多层方式分别训练神经网络，首先是正常和1型糖尿病，然后是正常和2型糖尿病，最后是健康和妊娠期糖尿病。多层神经网络的架构使分类更加有效。为了从敏感性、特异性和准确性方面提供糖尿病诊断的实验分析和性能，构建了一个混淆矩阵。该建议的多层神经网络获得了0.95和0.97的最大特异性和敏感性值。该模型对糖尿病分类的准确率为97%，优于其他模型，表明它是一种可行且有效的方法。
