Ismail Walaa N
Department of Management Information Systems, College of Business Administration, Al Yamamah University, Riyadh 11512, Saudi Arabia.
Diagnostics (Basel). 2023 Jul 27;13(15):2501. doi: 10.3390/diagnostics13152501.
Chronic kidney disease (CKD) refers to impairment of the kidneys that may worsen over time. Early detection of CKD is crucial for saving millions of lives. As a result, several studies are currently focused on developing computer-aided systems to detect CKD in its early stages. Manual screening is time-consuming and subject to personal judgment. Therefore, methods based on machine learning (ML) and automatic feature selection are used to support graders. The goal of feature selection is to identify the most relevant and informative subset of features in a given dataset. This approach helps mitigate the curse of dimensionality, reduce dimensionality, and enhance model performance. The use of natural-inspired optimization algorithms has been widely adopted to develop appropriate representations of complex problems by conducting a blackbox optimization process without explicitly formulating mathematical formulations. Recently, snake optimization algorithms have been developed to identify optimal or near-optimal solutions to difficult problems by mimicking the behavior of snakes during hunting. The objective of this paper is to develop a novel snake-optimized framework named CKD-SO for CKD data analysis. To select and classify the most suitable medical data, five machine learning algorithms are deployed, along with the snake optimization (SO) algorithm, to create an extremely accurate prediction of kidney and liver disease. The end result is a model that can detect CKD with 99.7% accuracy. These results contribute to our understanding of the medical data preparation pipeline. Furthermore, implementing this method will enable health systems to achieve effective CKD prevention by providing early interventions that reduce the high burden of CKD-related diseases and mortality.
慢性肾脏病（CKD）是指肾脏功能受损，且可能随时间恶化。早期发现CKD对于拯救数百万人的生命至关重要。因此，目前有几项研究专注于开发计算机辅助系统以在早期阶段检测CKD。人工筛查既耗时又受个人判断影响。所以，基于机器学习（ML）和自动特征选择的方法被用于辅助分级人员。特征选择的目标是在给定数据集中识别最相关且信息丰富的特征子集。这种方法有助于减轻维度灾难、降低维度并提高模型性能。自然启发式优化算法的使用已被广泛采用，通过进行黑箱优化过程来开发复杂问题的适当表示，而无需明确制定数学公式。最近，已开发出蛇优化算法，通过模仿蛇在捕猎时的行为来识别难题的最优或近似最优解。本文的目的是为CKD数据分析开发一种名为CKD - SO的新型蛇优化框架。为了选择和分类最合适的医学数据，部署了五种机器学习算法以及蛇优化（SO）算法，以创建对肾脏和肝脏疾病极其准确的预测。最终结果是一个能够以99.7%的准确率检测CKD的模型。这些结果有助于我们理解医学数据准备流程。此外，实施这种方法将使卫生系统能够通过提供早期干预措施来有效预防CKD，从而减轻与CKD相关疾病的高负担和死亡率。
