2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 10 , Issue 2 , PP: 38-48, 2023 | Cite this article as | XML | Html | PDF | Full Length Article
B. Narasimha Swamy 1 , Rajeswari Nakka 2 , Aditi Sharma 3 * , S. Phani Praveen 4 , Venkata Nagaraju Thatha 5 , Kumar Gautam 6
Doi: https://doi.org/10.54216/JISIoT.100204
Chronic kidney disease (CKD) is a common and possibly fatal condition affecting billions worldwide. Early detection and accurate diagnosis of CKD are critical for timely intervention and improved patient outcomes. In recent years, machine learning techniques have shown great promise in assisting medical professionals in detecting and diagnosing various diseases. This study aims to develop a novel machine learning (ML) model for detecting CKD using clinical and demographic data. The dataset used in this study comprises a comprehensive collection of patient records, including laboratory test results, medical history, and demographic information. Feature selection is one of the techniques that, combined with the ML approach, select the significant features. Several ML algorithms were implemented to detect CKD in the early stages but identified the issues with existing ML algorithms. The developed models' performance is assessed using precision, accuracy, and recall metrics. Additionally, feature importance analysis is conducted to identify the key factors influencing CKD diagnosis. The strength of the proposed approach shows accurately by identifying the individuals at risk of CKD and distinguishing between different stages of the disease. The dataset used for this research was collected from the UCI repository, which consists of 25 attributes, 550 samples, 400 CKD affected, and 150 standard models. The dataset consists of two folders, training and testing. The training utilizes 1000 samples with detailed patient health conditions. The developed CKD detection model shows promising results, achieving high accuracy of 97.98%. on the test dataset. By leveraging machine learning algorithms, this approach can assist healthcare professionals in making more informed decisions regarding early intervention and personalized treatment plans for patients with CKD. Ultimately, applying machine learning techniques in CKD detection can improve patient outcomes and reduce healthcare costs.
Chronic kidney disease (CKD) , Machine Learning (ML) , Support Vector Machines (SVM) , Random Forests , And Neural Networks
[1] M. Almasoud, T.E. Ward, "Detection of chronic kidney disease using machine learning algorithms with least number of predictors," Int J Soft Comput Appl., 10 (2019).
[2] R. Ani, G. Sasi, U.R. Sankar, O. Deepa, "Decision support system for diagnosis and prediction of chronic renal failure using random subspace classification." 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI), IEEE (2016), pp. 1287-1292.
[3] M. M. Hossain, R. K. Detwiler, E. H. Chang, M. C. Caughey, M. W. Fisher, T. C. Nichols, et al., " Mechanical anisotropy assessment in kidney cortex using ARFI peak displacement: Preclinical validation and pilot in vivo clinical results in kidney allografts ", IEEE Trans. Ultrason. Ferroelectr. Freq. Control, vol. 66, no. 3, pp. 551-562, Mar. 2019.
[4] Dash, R.K., Nguyen, T.N., Cengiz, K. et al. Fine-tuned support vector regression model for stock predictions. Neural Comput & Applic 35, 23295–23309 (2023). https://doi.org/10.1007/s00521-021-05842-w
[5] M. Arora, E.A. Sharma, "Chronic kidney disease detection by analyzing medical datasets in weka." Int J Comput Mach Learn Algor New Adv Mach Learn., 3 (2016), pp. 19-48.
[6] C. Anuradha, D. Swapna, B. Thati, V. N. Sree and S. P. Praveen, "Diagnosing for Liver Disease Prediction in Patients Using Combined Machine Learning Models," 2022 4th International Conference on Smart Systems and Inventive Technology (ICSSIT), Tirunelveli, India, 2022, pp. 889-896, doi: 10.1109/ICSSIT53264.2022.9716312.
[7] Prashant K. Jamwal, Aibek Niyetkaliyev, Shahid Hussain, Aditi Sharma, Paulette Van Vliet, Utilizing the intelligence edge framework for robotic upper limb rehabilitation in home, MethodsX, Volume 11, 2023, 102312, ISSN 2215-0161, https://doi.org/10.1016/j.mex.2023.102312.
[8] Sirisha, U., Praveen, S. P., Srinivasu, P. N., Barsocchi, P., & Bhoi, A. K. (2023). Statistical Analysis of Design Aspects of Various YOLO-Based Deep Learning Models for Object Detection. International Journal of Computational Intelligence Systems, 16(1), 126.
[9] Sirisha, U., & Sai Chandana, B. (2022). Semantic interdisciplinary evaluation of image captioning models. Cogent Engineering, 9(1), 2104333.
[10] Gajender Kumar,Vinod Patidar,Prolay Biswas,Mukta Patel,Chaur Singh Rajput,Anita Venugopal,Aditi Sharma. (2023). IOT enabled Intelligent featured imaging Bone Fractured Detection System. Journal of Intelligent Systems and Internet of Things, 9 ( 2 ), 08-22.
[11] Sirisha, U., Chandana, B. S., & Harikiran, J. (2023). NAM-YOLOV7: An Improved YOLOv7 Based on Attention Model for Animal Death Detection. Traitement du Signal, 40(2).
[12] Sirisha, U., & Chandana, B. S. (2023). Privacy preserving image encryption with optimal deep transfer learning based accident severity classification model. Sensors, 23(1), 519.
[13] A. Charleonnan, T. Fufaung, T. Niyomwong, W. Chokchueypattanakit, S. Suwannawach, N. Ninchawee, "Predictive analytics for chronic kidney disease using machine learning techniques." 2016 Management and Innovation Technology International Conference (MITicon), IEEE (2016) pp. MIT–80.
[14] Rahul Sharma ,Shiv Shakti Shrivastava ,Aditi Sharma. (2023). Predicting Student Performance Using Educational Data Mining and Learning Analytics Technique. Journal of Intelligent Systems and Internet of Things, 10 ( 2 ), 24-37.
[15] Dionisio Ponce Ruiz,Rita Azucena D. Vasquez,Bolivar Villalta Jadan. (2023). Predictive Energy Management in Internet of Things: Optimization of Smart Buildings for Energy Efficiency. Journal of Intelligent Systems and Internet of Things, 10 ( 2 ), 08-17.
[16] Z. Chen, X. Zhang, Z. Zhang, "Clinical risk assessment of patients with chronic kidney disease by using clinical data and multivariate models." Int Urol Nephrol., 48 (2016), pp. 2069-2075.
[17] Praveen, S. P., Jyothi, V. E., Anuradha, C., VenuGopal, K., Shariff, V., & Sindhura, S. (2022). Chronic Kidney Disease Prediction Using ML-Based Neuro-Fuzzy Model. International Journal of Image and Graphics, 2340013.
[18] A.M. Cueto-Manzano, L. Cortés-Sanabria, H.R. Martínez-Ramírez, E. Rojas-Campos, B. Gómez-Navarro, M. Castillero-Manzano, "Prevalence of chronic kidney disease in an adult population." Arch Med Res., 45 (2014), pp. 507-513.
[19] Swamy, S. R., Praveen, S. P., Ahmed, S., Srinivasu, P. N., & Alhumam, A. (2023). Multi-features disease analysis based smart diagnosis for covid-19. Computer Systems Science and Engineering, 45(1), 869-886.
[20] Goar, V., Sharma, A., Yadav, N.S. et al. IoT-Based Smart Mask Protection against the Waves of COVID-19. J Ambient Intell Human Comput 14, 11153–11164 (2023). https://doi.org/10.1007/s12652-022-04395-7
[21] K. Eroğlu, T. Palabaş, "The impact on the classification performance of the combined use of different classification methods and different ensemble algorithms in chronic kidney disease detection." 2016 National Conference on Electrical, Electronics and Biomedical Engineering (ELECO), IEEE (2016), pp. 512-516.
[22] S. Phani Praveen,Balamuralikrishna Thati,Ch Anuradha,S. Sindhura,Mohammed Altaee,M. Abdul jalil, A Novel Approach for Enhance Fusion Based Healthcare System In Cloud Computing, Journal of Intelligent Systems and Internet of Things, Vol. 9 , No. 1 , (2023) : 84-96 (Doi : https://doi.org/10.54216/JISIoT.090106)..
[23] Aruna, R., Kushwah, V.S., Praveen, S.P. et al. Coalescing novel QoS routing with fault tolerance for improving QoS parameters in wireless Ad-Hoc network using craft protocol. Wireless Netw (2023). https://doi.org/10.1007/s11276-023-03515-1.
[24] Ashish Patel,Richa Mishra ,Aditi Sharma. (2023). Maize Plant Leaf Disease Classification Using Supervised Machine Learning Algorithms. Fusion: Practice and Applications, 13 ( 2 ), 08-21.
[25] A. Madhuri,Veerapaneni Esther Jyothi,S. Phani Praveen,Mustafa Altaee,Ibrahim N. Abdullah, Granulation-Based Data Fusion Approach for a Critical Thinking Worldview Information Processing, Journal of Intelligent Systems and Internet of Things, Vol. 9 , No. 1 , (2023) : 49-68 (Doi : https://doi.org/10.54216/JISIoT.090104).
[26] M A Abdel-Fattah, Nermin Abdelhakim Othman, Nagwa Goher, "Predicting Chronic Kidney Disease Using Hybrid Machine Learning Based on Apache Spark", Computational Intelligence and Neuroscience, vol. 2022.
[27] J. Qin, L. Chen, Y. Liu, C. Liu, C. Feng and B. Chen, "A Machine Learning Methodology for Diagnosing Chronic Kidney Disease," in IEEE Access, vol. 8, pp. 20991-21002, 2020, doi: 10.1109/ACCESS.2019.2963053.
[28] N. Alapati et al., "Cardiovascular Disease Prediction using machine learning," 2022 International Conference on Fourth Industrial Revolution Based Technology and Practices (ICFIRTP), Uttarakhand, India, 2022, pp. 60-66, doi: 10.1109/ICFIRTP56122.2022.10059422.
[29] Sirisha, U., & Bolem, S. C. (2022). Aspect based sentiment & emotion analysis with ROBERTa, LSTM. International Journal of Advanced Computer Science and Applications, 13(11).
