Abstract

Chronic kidney disease (CKD) is a progressive disorder that worsens over time, leading to a variety of major health problems including hypertension, anaemia, nerve damage, fractured bones, and cardiovascular diseases. This rate is growing globally by the development of ageing and diabetes and hypertension. Precise prediction of CKD may aid healthcare practitioners in developing tailored treatment plans that address the distinct underlying causes of the illness, reduce the likelihood of complications, and improve patient outcomes. Predicting the course of CKD is crucial because early detection and accurate diagnosis of CKD may improve patient outcomes, prevent complications, and save medical costs. Machine learning (ML) based techniques are taken into consideration for CKD prediction. Several ML algorithms are used, including decision tree (DT), neural networks (NN), random forest (RF), XGBoost (XGB), Gaussian Naive Bayes (GNB), and CatBoost (CB) Classifier. Using six distinct ML algorithms, the kidney disease dataset is used to carry out the empirical method. Different validity ratings are used to evaluate and contrast the generated findings. The results show RF, XGB and CB get higher accuracy of 95 % which is better suited in predicting CKD than DT, NN, and GNB. These algorithms illustrate the highest accuracy and efficiency in the diagnosis of the patients at the beginning CKD stage. An accurate prediction of CKD enables practitioners in the healthcare sector to develop individualized treatment plan, avert the occurrence of complications and achieve a better patient outcome. Early recognition and intervention based on predictive models can prevent unproductive testing, treatments, and hospital beds while also responding quickly to disease management. The predictive models of CKD can identify high-risk patients and facilitate early interventions, ultimately enhancing public health outcomes.

Keyword

Machine learning, Chronic kidney disease, Random Forest, XGBoost, CatBoost, Healthcare.

Cite this article

Yamini B, Saraswathi T, Radhakrishnan P, Nalini M, Shanmuganathan M, Subramanian. SR.Machine learning algorithms for predicting of chronic kidney disease and its significance in healthcare. International Journal of Advanced Technology and Engineering Exploration. 2024;11(112):388-404. DOI:10.19101/IJATEE.2023.10101788

Refference

[1]Wang W, Chakraborty G, Chakraborty B. Predicting the risk of chronic kidney disease (CKD) using machine learning algorithm. Applied Sciences. 2020; 11(1):1-17.

[2]Wickramasinghe MP, Perera DM, Kahandawaarachchi KA. Dietary prediction for patients with chronic kidney disease (CKD) by considering blood potassium level using machine learning algorithms. In life sciences conference 2017 (pp. 300-3). IEEE.

[3]Gunarathne WH, Perera KD, Kahandawaarachchi KA. Performance evaluation on machine learning classification techniques for disease classification and forecasting through data analytics for chronic kidney disease (CKD). In 17th international conference on bioinformatics and bioengineering 2017 (pp. 291-6). IEEE.

[4]Elhoseny M, Shankar K, Uthayakumar J. Intelligent diagnostic prediction and classification system for chronic kidney disease. Scientific reports. 2019; 9(1):1-14.

[5]Rajendran T, Rajathi SA, Balakrishnan C, Aswini J, Prakash RB, Subramanian RS. Risk prediction modeling for breast cancer using supervised machine learning approaches. In 2nd international conference on automation, computing and renewable systems 2023 (pp. 702-8). IEEE.

[6]Raju NG, Lakshmi KP, Praharshitha KG, Likhitha C. Prediction of chronic kidney disease (CKD) using data science. In international conference on intelligent computing and control systems 2019 (pp. 642-7). IEEE.

[7]Sinha P, Sinha P. Comparative study of chronic kidney disease prediction using KNN and SVM. International Journal of Engineering Research and Technology. 2015; 4(12):608-12.

[8]Almansour NA, Syed HF, Khayat NR, Altheeb RK, Juri RE, Alhiyafi J, et al. Neural network and support vector machine for the prediction of chronic kidney disease: a comparative study. Computers in biology and medicine. 2019; 109:101-11.

[9]Pasadana IA, Hartama D, Zarlis M, Sianipar AS, Munandar A, Baeha S, et al. Chronic kidney disease prediction by using different decision tree techniques. In journal of physics: conference series 2019 (pp. 1-6). IOP Publishing.

[10]Saha A, Saha A, Mittra T. Performance measurements of machine learning approaches for prediction and diagnosis of chronic kidney disease (CKD). In proceedings of the 7th international conference on computer and communications management 2019 (pp. 200-4). ACM.

[11]Baidya D, Umaima U, Islam MN, Shamrat FJ, Pramanik A, Rahman MS. A deep prediction of chronic kidney disease by employing machine learning method. In 6th international conference on trends in electronics and informatics 2022 (pp. 1305-10). IEEE.

[12]Srivastava S, Yadav RK, Narayan V, Mall PK. An ensemble learning approach for chronic kidney disease classification. Journal of Pharmaceutical Negative Results. 2022:2401-9.

[13]Dritsas E, Trigka M. Machine learning techniques for chronic kidney disease risk prediction. Big Data and Cognitive Computing. 2022; 6(3):1-15.

[14]Singh V, Asari VK, Rajasekaran R. A deep neural network for early detection and prediction of chronic kidney disease. Diagnostics. 2022; 12(1):1-22.

[15]Abdel-fattah MA, Othman NA, Goher N. Predicting chronic kidney disease using hybrid machine learning based on apache spark. Computational Intelligence and Neuroscience. 2022; 2022:1-12.

[16]Kashyap CP, Reddy GS, Balamurugan M. Prediction of chronic disease in kidneys using machine learning classifiers. In 1st international conference on computational science and technology 2022 (pp. 562-67). IEEE.

[17]Yogish HK. Prediction of chronic kidney disease using machine learning technique. In fourth international conference on cognitive computing and information processing 2022 (pp. 1-6). IEEE.

[18]Bhowmick R, VM AX. Machine learning models for analysis and prediction of chronic kidney disease. In 9th international conference on advanced computing and communication systems 2023 (pp. 937-42). IEEE.

[19]Hassan R, Sharan B, Kumari N, Rafiq T, Thakur G, Bhargav R. Prediction of chronic diseases using machine learning classifiers. In 10th international conference on computing for sustainable global development 2023 (pp. 885-9). IEEE.

[20]Swain D, Patel H, Patel K, Sakariya V, Chaudhari N. An intelligent clinical support system for the early diagnosis of the chronic kidney disease. In 2nd international symposium on sustainable energy, signal processing and cyber security 2022 (pp. 1-5). IEEE.

[21]Anil D, Naimudden S, Reddy AS, Lavanya A. Prediction of chronic kidney disease using various machine learning algorithms. In international conference on innovative data communication technologies and application 2023 (pp. 156-61). IEEE.

[22]Vimala C, Subramani C, Bhagat V, Sravani MV. Analysis of different algorithms to predict chronic kidney disease. In international interdisciplinary humanitarian conference for sustainability 2022 (pp. 1051-4). IEEE.

[23]Navyasree V, Surarchitha Y, Reddy AM, Sree BD, Anuhya A, Jabeen H. Predicting the risk factor of kidney disease using meta classifiers. In 2nd Mysore sub section international conference 2022 (pp. 1-6). IEEE.

[24]Deepa R, Priscilla R, Pandi A, Renukadevi B. An early prediction model for chronic kidney disease using machine learning. In international conference on networking and communications 2023 (pp. 1-10). IEEE.

[25]Pilli VS, Pamidi K, Poovammal E. Chronic kidney disease prediction. In international conference for advancement in technology 2023 (pp. 1-4). IEEE.

[26]Raj RS, Kusuma M. A comprehensive analysis of chronic health diseases using big data. In international conference on evolutionary algorithms and soft computing techniques 2023 (pp. 1-5). IEEE.

[27]Saumya L, Manimozhi I. A survey on machine learning algorithms for the detection of chronic kidney disease. In 2nd international conference on automation, computing and renewable systems 2023 (pp. 1832-7). IEEE.

[28]NJ S, Venkatesh K. CKD prediction using mixed dataset concept and deep learning: a combined approach for accurate diagnosis. In international conference on research methodologies in knowledge management, artificial intelligence and telecommunication engineering 2023 (pp. 1-6). IEEE.

[29]Jaya LA, Pm JP, Lokesh K, Dev AD, Jagathieshwaran J, Sneha M. Predicting the severity of chronic kidney disease with J48. International conference on computing, communication, and intelligent systems 2023 (pp.556-61). IEEE.

[30]Saraswat T, Pathak S, Sachdeva S, Sahu K, Sawhney R. Kidney disease detection and identification using artificial intelligence. In international conference on cloud computing, data science & engineering 2023 (pp. 537-43). IEEE.

[31]Rysz J, Gluba-brzózka A, Franczyk B, Jabłonowski Z, Ciałkowska-rysz A. Novel biomarkers in the diagnosis of chronic kidney disease and the prediction of its outcome. International Journal of Molecular Sciences. 2017; 18(8):1-17.

[32]Poorani K, Karuppasamy M. Comparative analysis of chronic kidney disease prediction using supervised machine learning techniques. In international conference on information and communication technology for intelligent systems 2023 (pp. 87-95). Singapore: Springer Nature Singapore.

[33]Rubini L. Early stage of chronic kidney disease UCI machine learning repository. Chronic Kidney Disease. 2015.

[34]Gracious LA, Jasmine RM, Pooja E, Anish TP, Johncy G, Subramanian RS. Machine learning and deep learning transforming healthcare: an extensive exploration of applications, algorithms, and prospects. In 4th IEEE global conference for advancement in technology 2023 (pp. 1-6). IEEE.

[35]Yamini B, Kaneti VR, Nalini M, Subramanian S. Machine learning-driven PCOS prediction for early detection and tailored interventions. SSRG International Journal of Electrical and Electronics Engineering. 2023; 10:61-75.

[36]Bansal M, Goyal A, Choudhary A. A comparative analysis of K-nearest neighbor, genetic, support vector machine, decision tree, and long short term memory algorithms in machine learning. Decision Analytics Journal. 2022; 3:100071.

[37]Verma VK, Verma S. Machine learning applications in healthcare sector: an overview. Materials Today: Proceedings. 2022; 57:2144-7.

[38]Humayun M, Sujatha R, Almuayqil SN, Jhanjhi NZ. A transfer learning approach with a convolutional neural network for the classification of lung carcinoma. Healthcare 2022; 10:1-15. MDPI.

[39]Nithya T, Kumar VN, Gayathri S, Deepa S, Varun CM, Subramanian RS. A comprehensive survey of machine learning: advancements, applications, and challenges. In second international conference on augmented intelligence and sustainable systems 2023 (pp. 354-61). IEEE.

[40]Kamala SP, Gayathri S, Pillai NM, Gracious LA, Varun CM, Subramanian RS. Predictive analytics for heart disease detection: a machine learning approach. In 4th international conference on electronics and sustainable communication systems 2023 (pp. 1583-9). IEEE.

[41]Yamini B, Sudha K, Nalini M, Kavitha G, Subramanian RS, Sugumar R. Predictive modelling for lung cancer detection using machine learning techniques. In 8th international conference on communication and electronics systems 2023 (pp. 1220-6). IEEE.

[42]Subramanian RS, Prabha D. Ensemble variable selection for naive bayes to improve customer behaviour analysis. Computer Systems Science & Engineering. 2022; 41(1):339-55.

[43]Kamel H, Abdulah D, Al-tuwaijari JM. Cancer classification using gaussian naive Bayes algorithm. In international engineering conference 2019 (pp. 165-70). IEEE.

[44]Luo M, Wang Y, Xie Y, Zhou L, Qiao J, Qiu S, et al. Combination of feature selection and catboost for prediction: the first application to the estimation of aboveground biomass. Forests. 2021; 12(2):1-21.

[45]Subramanian RS, Sudha K, Ramana KV, Sivakumar S, Nithyanandhan R, Nalini M. Hybrid variable selection approach to analyse high dimensional dataset. In 7th international conference on computing methodologies and communication 2023 (pp. 1489-95). IEEE.

[46]Raju SS, Dhandayudam P. Prediction of customer behaviour analysis using classification algorithms. In AIP conference proceedings 2018. AIP Publishing.