Department of Computer Science and Engineering, International Institute of Information Technology, Bhubaneswar, Odisha, India.
J Integr Bioinform. 2020 Aug 13;18(1):81-99. doi: 10.1515/jib-2019-0097.
As stated by World Health Organization (WHO) report, 246 million individuals have suffered with diabetes disease over worldwide and it is anticipated that by 2025 this estimation can cross 380 million. So, the proper and quick diagnosis of this disease is turned into a significant challenge for the machine learning researchers. This paper aims to design a robust model for diagnosis of diabetes using a hybrid approach of Chaotic-Jaya (CJaya) algorithm with Extreme Learning Machine (ELM), which is named as CJaya-ELM. In this paper, Jaya algorithm with Chaotic learning approach is used to optimize the random parameters of ELM classifier. Here, to assess the efficacy of the designed model, Pima Indian diabetes dataset is considered. Here, the designed model CJaya-ELM, has been compared with basic ELM, Teaching Learning Based Optimization algorithm (TLBO) optimized ELM (TLBO-ELM), Multi-Layer Perceptron (MLP), Jaya algorithm optimized MLP (Jaya-MLP), TLBO algorithm optimized MLP (TLBO-MLP) and CJaya algorithm optimized MLP models. CJaya-ELM model resulted in the highest testing accuracy of 0.9687, sensitivity of 1, specificity of 0.9688 with 0.9782 area under curve (AUC) value. Results reveal that CJaya-ELM model effectively classifies both the positive and negative samples of Pima and outperforms the competitors.
世界卫生组织（WHO）报告称，全球有 2.46 亿人患有糖尿病，预计到 2025 年，这一数字将超过 3.8 亿。因此，对这种疾病进行正确和快速的诊断成为机器学习研究人员的一项重大挑战。本文旨在设计一种使用混沌 Jaya（CJaya）算法与极限学习机（ELM）的混合方法诊断糖尿病的稳健模型，该模型命名为 CJaya-ELM。在本文中，使用具有混沌学习方法的 Jaya 算法来优化 ELM 分类器的随机参数。这里，为了评估设计模型的效果，考虑了皮马印第安人糖尿病数据集。在这里，所设计的模型 CJaya-ELM 与基本的 ELM、基于教学的优化算法（TLBO）优化的 ELM（TLBO-ELM）、多层感知器（MLP）、Jaya 算法优化的 MLP（Jaya-MLP）、TLBO 算法优化的 MLP（TLBO-MLP）和 CJaya 算法优化的 MLP 模型进行了比较。CJaya-ELM 模型的测试准确率最高，为 0.9687，灵敏度为 1，特异性为 0.9688，曲线下面积（AUC）值为 0.9782。结果表明，CJaya-ELM 模型能够有效地对皮马的阳性和阴性样本进行分类，并且优于竞争对手。
