Indonesian Journal of Electrical Engineering and Computer Science Vol. 34, No. 1, April 2024, pp. 144~151 ISSN: 2502-4752, DOI: 10.11591/ijeecs.v34.i1.pp144-151  144 Journal homepage: http://ijeecs.iaescore.com CNN-CatBoost ensemble deep learning model for enhanced disease detection and classification of kidney disease Navaneeth Bhaskar 1 , Ratnaprabha Ravindra Borhade 2 , Sheetal Barekar 3 , Mrinal Bachute 4 , Vinayak Bairagi 5 1 Department of Computer Science and Engineering (Data Science), Sahyadri College of Engineering and Management, Mangalore, India 2 Department of Electronics and Telecommunication Engineering, Cummins College of Engineering for Women, Pune, India 3 Department of Computer Engineering, Cummins College of Engineering for Women, Pune, India 4 Symbiosis Institute of Technology, Symbiosis International University, Pune, India 5 Department of Electronics and Telecommunication Engineering, AISSMS Institute of Information Technology, Pune, India Article Info ABSTRACT Article history: Received Nov 15, 2023 Revised Jan 7, 2024 Accepted Jan 11, 2024 An efficient deep-learning prediction model for identifying chronic kidney disease (CKD) from exhaled breath is presented in this paper. The concentration of urea will be higher in CKD patients. Salivary urease breaks down the stored urea into ammonia, which is then excreted through breath. Thus, by monitoring the breath ammonia content, it is possible to identify the presence of high urea levels in the body. In this work, a novel sensing module is developed and applied to measure and assess the amount of ammonia in exhaled breath. Moreover, an effective deep learning prediction model that combines the CatBoost algorithm and convolutional neural network (CNN) is used to automate the prediction of disease. The proposed model, which combines the benefits of gradient-boosting and CNN, attained an exceptional accuracy of 98.37%. Experiments are conducted to evaluate the proposed model using real-time data and to assess how well it performs in comparison with existing deep learning methods. Our study's findings demonstrate that kidney disease can be accurately and noninvasively diagnosed using the proposed approach. Keywords: CatBoost Convolutional neural network Deep learning Exhaled breath Kidney disease This is an open access article under the CC BY-SA license. Corresponding Author: Navaneeth Bhaskar Department of Computer Science and Engineering (Data Science) Sahyadri College of Engineering and Management Mangalore, India Email: navbskr@gmail.com 1. INTRODUCTION Kidney disease is the term used to describe damage to the kidneys that makes it difficult for them to filter wastes as efficiently as they should. Insufficient kidney function results in an accumulation of waste products and excess fluid in the circulation, leading to imbalances and toxins in the body [1]. Chronic kidney disease (CKD) develops over several months or years. A steady decline in kidney function is an indicator of end-stage renal disease, in which the kidneys completely fail and require dialysis or a kidney transplant to survive. Common causes of CKD include diabetes, hypertension, and glomerulonephritis. The signs and symptoms of kidney damage can include fatigue, edema, shortness of breath, nausea and confusion [2]. Depending on the type and severity of renal failure, there are a variety of treatment options available, including treating the underlying cause, changing one's diet, taking medication, and in severe cases, undergoing kidney replacement therapy [3]. Diagnosing CKD frequently involves a combination of laboratory testing, physical examination, and medical history. There are numerous biomarkers, or