International Journal of Electrical and Computer Engineering (IJECE) Vol. 14, No. 4, August 2024, pp. 4775~4790 ISSN: 2088-8708, DOI: 10.11591/ijece.v14i4.pp4775-4790  4775 Journal homepage: http://ijece.iaescore.com Artificial intelligence for early-stage detection of chronic kidney disease Mamatha B. 1 , Sujatha P. Terdal 2 1 Department of Computer Science and Engineering (Artificial Intelligence and Machine Learning), CMR Technical Campus, Hyderabad, India 2 Computer Science and Engineering, PDA College of Engineering Gulbarga, Kalaburagi, India Article Info ABSTRACT Article history: Received Jan 3, 2024 Revised Mar 26, 2024 Accepted Apr 16, 2024 Early-stage detection of chronic kidney disease (CKD) is crucial in research to enable timely intervention, enhance understanding of disease progression, reduce healthcare costs and support public health initiatives. The traditional approaches on early-stage chronic kidney disease detection often suffer from slow convergence and not integrate advanced technologies, impacting their effectiveness. Additionally, security and privacy concerns related to patient data are ineffectively addressed. To overcome these issues, this research incorporates novel optimized artificial intelligence-based approaches. The main aim is to enhance detection process through enhanced hybrid mud ring network (EHMRN), a novel detection technique combining light gradient boosting machine and MobileNet, involving extensive data collection, including a large dataset of 100,000 instances. The introduced network is optimized through the mud ring optimization to attain enhanced performance. Incorporating spark ensures secure cloud-based storage, enhancing privacy and compliance with healthcare data regulations. This approach represents a significant advancement in primary stage detection more effectively and promptly. The results show that the introduced approach outperforms traditional approaches in terms of accuracy (99.96%), F1-score (99.91%), precision (100%), specificity (99.98%), recall (100%) and execution time (0.09s). Keywords: Apache spark Artificial intelligence Big data analysis Chronic kidney disease Enhanced hybrid network This is an open access article under the CC BY-SA license. Corresponding Author: Mamatha B. Department of Computer Science and Engineering (Artificial Intelligence and Machine Learning), CMR Technical Campus Hyderabad, Andhra Pradesh-501401, India Email: mamatha.789@gmail.com 1. INTRODUCTION Chronic kidney disease (CKD) poses a significant global health challenge, characterized by a gradual decline in kidney function. Despite its asymptomatic nature, CKD progress silently to advanced stages, leading to severe complications and increased healthcare burdens. The imperative for early-stage CKD detection is underscored by its potential to substantially improve patient outcomes through timely intervention and management [1]. Recognizing the importance of this issue, this research seeks to address existing challenges in CKD detection by employing a novel big data analytics approach [2], [3]. Traditional methods for detecting CKD encounter significant challenges when faced with large- scale datasets, managing missing data, and selecting optimal features. These limitations hamper their effectiveness in early detection. Recent advancements in machine learning (ML) and deep learning (DL) have introduced various techniques tailored for early-stage detection of chronic kidney disease (ESDCKD).