Rankovic Nevena, Rankovic Dragica, Lukic Igor
Department of Cognitive Science and Artificial Intelligence, School of Humanities and Digital Sciences, Tilburg University, 5037 AB Tilburg, The Netherlands.
Department of Mathematics, Informatics and Statistics, Faculty of Applied Sciences, Union University "Nikola Tesla", 18000 Nis, Serbia.
Diagnostics (Basel). 2023 Feb 20;13(4):798. doi: 10.3390/diagnostics13040798.
Hyperinsulinemia is a condition characterized by excessively high levels of insulin in the bloodstream. It can exist for many years without any symptomatology. The research presented in this paper was conducted from 2019 to 2022 in cooperation with a health center in Serbia as a large cross-sectional observational study of adolescents of both genders using datasets collected from the field. Previously used analytical approaches of integrated and relevant clinical, hematological, biochemical, and other variables could not identify potential risk factors for developing hyperinsulinemia. This paper aims to present several different models using machine learning (ML) algorithms such as naive Bayes, decision tree, and random forest and compare them with a new methodology constructed based on artificial neural networks using Taguchi's orthogonal vector plans (ANN-L), a special extraction of Latin squares. Furthermore, the experimental part of this study showed that ANN-L models achieved an accuracy of 99.5% with less than seven iterations performed. Furthermore, the study provides valuable insights into the share of each risk factor contributing to the occurrence of hyperinsulinemia in adolescents, which is crucial for more precise and straightforward medical diagnoses. Preventing the risk of hyperinsulinemia in this age group is crucial for the well-being of the adolescents and society as a whole.
高胰岛素血症是一种以血液中胰岛素水平过高为特征的病症。它可能存在多年而无任何症状。本文所呈现的研究于2019年至2022年期间，与塞尔维亚的一家健康中心合作开展，是一项针对青少年的大型横断面观察性研究，使用从实地收集的数据集。先前使用的整合相关临床、血液学、生化及其他变量的分析方法无法识别出发生高胰岛素血症的潜在风险因素。本文旨在展示几种使用机器学习（ML）算法（如朴素贝叶斯、决策树和随机森林）的不同模型，并将它们与一种基于人工神经网络使用田口正交向量规划（ANN-L）构建的新方法进行比较，ANN-L是拉丁方的一种特殊提取。此外，本研究的实验部分表明，ANN-L模型在执行少于七次迭代的情况下达到了99.5%的准确率。此外，该研究为每个风险因素在青少年高胰岛素血症发生中所占的比例提供了有价值的见解，这对于更精确和直接的医学诊断至关重要。预防该年龄组高胰岛素血症的风险对于青少年乃至整个社会的福祉至关重要。
