Improved ResNet Models for Chronic Kidney Disease Prediction
Main Article Content
Abstract
Chronic kidney disease (CKD), a consequential health issue that can deeply affect an individual's overall wellness, can be initiated by either kidney cancer or a gradual reduction in kidney function. As the chronic disease advances, it can reach a critical stage where only dialysis or surgery can save lives. Halting its progress becomes crucial. CKD patients also face a heightened risk of premature death. Early detection of associated conditions poses a challenging task for healthcare professionals aiming to prevent their onset. A unique deep learning model is presented in this work for the prediction of CKD. Many existing CKD prediction models have the drawbacks of producing less accuracy, mispredicting, utilizing more computation time, and using low-quality datasets or data with noise and missing values, leading to misprediction. So it is necessary to develop new techniques that give high predictions with less computation time. The objective of this research work is to build improved ResNet models for the prediction of chronic kidney disease and evaluate their performance in comparison to other cutting-edge machine learning and deep learning methods. This research work developed ResNet models such as improved ResNet 152v2 with inception, improved ResNet 101, and improved ResNet50 models that produced 99.90%, 96.53%, and 93.968% accuracy, respectively. The proposed ResNet models for CKD prediction will be useful to nephrologists and other medical professionals.
Article Details
This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License.
References
Z. Chen, X. Zhang, and Z. Zhang, “Clinical risk assessment of patients with chronic kidney disease by using clinical data and multivariate models,” Int. Urol. Nephrol., vol. 48, no. 12, pp. 2069–2075, 2016, doi: 10.1007/s11255-016-1346-4.
C. George, A. Mogueo, I. Okpechi, J. B. Echouffo-Tcheugui, and A. P. Kengne, “Chronic kidney disease in low-income to middle-income countries: The case f increased screening,” BMJ Glob. Heal., vol. 2, no. 2, pp. 1–10, 2017, doi: 10.1136/bmjgh-2016-000256.
K. T. Mills et al., “A systematic analysis of worldwide population-based data on the global burden of chronic kidney disease in 2010,” Kidney Int., vol. 88, no. 5, pp. 950–957, 2015, doi: 10.1038/ki.2015.230.
N. Buza and M. Dizdar, “Cmbebih 2017,” vol. 62, no. Ml, 2017, doi: 10.1007/978-981-10-4166-2.
L. Zhang et al., “Prevalence of chronic kidney disease in China: A cross-sectional survey,” Lancet, vol. 379, no. 9818, pp. 815–822, 2012, doi: 10.1016/S0140-6736(12)60033-6.
J. He, J. Lin, and M. Duan, “Application of Machine Learning to Predict Acute Kidney Disease in Patients With Sepsis Associated Acute Kidney Injury,” Front. Med., vol. 8, no. December, pp. 1–11, 2021, doi: 10.3389/fmed.2021.792974.
A. M. El Nahas and A. K. Bello, “Chronic kidney disease: The global challenge,” Lancet, vol. 365, no. 9456, pp. 331–340, 2005, doi: 10.1016/S0140-6736(05)17789-7.
C.-H. Lin, Y.-C. Chang, and L.-M. Chuang, “Early detection of diabetic kidney disease: Present limitations and future perspectives,” World J. Diabetes, vol. 7, no. 14, p. 290, 2016, doi: 10.4239/wjd.v7.i14.290.
L. F., V. L.D., and P. P., “The importance of early detection of chronic kidney disease,” Nephrol. Dial. Transplant., vol. 17, no. SUPPL. 11, pp. 2–7, 2002, [Online]. Available: http://ovidsp.ovid.com/ovidweb.cgi?T=JS&PAGE=reference&D=emed5&NEWS=N&AN=2002397414.
W. M. Jwaid, “Image Processing Technology and Deep Learning Application: In Relation to the Context of Laser Positioning,” J. Phys. Conf. Ser., vol. 1879, no. 3, 2021, doi: 10.1088/1742-6596/1879/3/032130.
X. Wang and S. Li, “Image saliency prediction by learning deep probability model,” Signal Process. Image Commun., vol. 78, no. July, pp. 471–476, 2019, doi: 10.1016/j.image.2019.08.002.
N. T. Rao, “Prediction and Forecasting of Persistent Kidney Problems Using Machine Prediction and Forecasting of Persistent Kidney Problems Using Machine Learning Algorithms,” no. October, 2020, doi: 10.31782/IJCRR.2020.122031.
F. Nie and J. Li, “Image Object Background Classification with Jensen-Shannon Divergence,” IAENG Int. J. Appl. Math., vol. 52, no. 4, 2022.
V. Kunwar, K. Chandel, A. S. Sabitha, and A. Bansal, “Chronic Kidney Disease analysis using data mining classification techniques,” Proc. 2016 6th Int. Conf. - Cloud Syst. Big Data Eng. Conflu. 2016, pp. 300–305, 2016, doi: 10.1109/CONFLUENCE.2016.7508132.
S. Bala and K. Kumar, “A Literature Review on Kidney Disease Prediction using Data Mining Classification Technique,” Int. J. Comput. Mob. Comput., vol. 3, no. 7, pp. 960–967, 2014, [Online]. Available: www.ijcsmc.com.
E. M. Senan et al., “Diagnosis of Chronic Kidney Disease Using Effective Classification Algorithms and Recursive Feature Elimination Techniques,” vol. 2021, 2021.
H. Polat, H. D. Mehr, and A. Cetin, “Diagnosis of Chronic Kidney Disease Based on Support Vector Machine by Feature Selection Methods,” 2017, doi: 10.1007/s10916-017-0703-x.
A. Saha, “Performance Measurements of Machine Learning Approaches for Prediction and Diagnosis of Chronic Kidney Disease ( CKD ),” pp. 200–204, 2019.
A. Haratian, Z. Maleki, F. Shayegh, and A. Safaeian, “Detection of factors affecting kidney function using machine learning methods,” Sci. Rep., no. 0123456789, pp. 1–15, 2022, doi: 10.1038/s41598-022-26160-8.
A. I. Technology et al., “OPTIMUM FEATURE SELECTION BASED BREAST CANCER PREDICTION USING MODIFIED LOGISTIC,” vol. 101, no. 8, pp. 2895–2908, 2023.
D. Vetrithangam, V. Senthilkumar, Neha, A. R. Kumar, P. N. Kumar, and M. Sharma, “Coronary Artery Disease Prediction Based on Optimal Feature Selection Using Improved Artificial Neural Network With Meta-Heuristic Algorithm,” J. Theor. Appl. Inf. Technol., vol. 100, no. 24, pp. 4771–4782, 2022.
D. Vetrithangam, V. Indira, S. Umar, B. Pant, M. K. Goyal, and B. Arunadevi, “Discriminating the Pneumonia-Positive Images from COVID-19-Positive Images Using an Integrated Convolutional Neural Network,” Math. Probl. Eng., vol. 2022, 2022, doi: 10.1155/2022/5643977.
C. C. Kuo et al., “Automation of the kidney function prediction and classification through ultrasound-based kidney imaging using deep learning,” npj Digit. Med., vol. 2, no. 1, 2019, doi: 10.1038/s41746-019-0104-2.
J. Zhao et al., “An early prediction model for chronic kidney disease,” Sci. Rep., vol. 12, no. 1, pp. 1–9, 2022, doi: 10.1038/s41598-022-06665-y.
T. Mathew and O. Corso, “Review article: Early detection of chronic kidney disease in Australia: Which way to go?,” Nephrology, vol. 14, no. 4, pp. 367–373, 2009, doi: 10.1111/j.1440-1797.2009.01113.x.
F. Ma, T. Sun, L. Liu, and H. Jing, “Detection and diagnosis of chronic kidney disease using deep learning-based heterogeneous modified artificial neural network,” Futur. Gener. Comput. Syst., vol. 111, pp. 17–26, 2020, doi: 10.1016/j.future.2020.04.036.
S. A. Alsuhibany et al., “Ensemble of Deep Learning Based Clinical Decision Support System for Chronic Kidney Disease Diagnosis in Medical Internet of Things Environment,” Comput. Intell. Neurosci., vol. 2021, no. Cc, 2021, doi: 10.1155/2021/4931450.
A. Raj et al., “Deep Learning-Based Total Kidney Volume Segmentation in Autosomal Dominant Polycystic Kidney Disease Using Attention, Cosine Loss, and Sharpness Aware Minimization,” Diagnostics, vol. 12, no. 5, 2022, doi: 10.3390/diagnostics12051159.
K. Shankar, P. Manickam, G. Devika, and M. Ilayaraja, “Optimal Feature Selection for Chronic Kidney Disease Classification using Deep Learning Classifier,” 2018 IEEE Int. Conf. Comput. Intell. Comput. Res. ICCIC 2018, pp. 1–5, 2018, doi: 10.1109/ICCIC.2018.8782340.
K. Kumar, M. Pradeepa, M. Mahdal, S. Verma, M. V. L. N. RajaRao, and J. V. N. Ramesh, “A Deep Learning Approach for Kidney Disease Recognition and Prediction through Image Processing,” Appl. Sci., vol. 13, no. 6, p. 3621, 2023, doi: 10.3390/app13063621.
A. J. Aljaaf et al., “Early Prediction of Chronic Kidney Disease Using Machine Learning Supported by Predictive Analytics,” 2018 IEEE Congr. Evol. Comput. CEC 2018 - Proc., pp. 1–9, 2018, doi: 10.1109/CEC.2018.8477876.
H. M. Zawbaa, E. Emary, and B. Parv, “Feature selection based on antlion optimization algorithm,” Proc. 2015 IEEE World Conf. Complex Syst. WCCS 2015, 2016, doi: 10.1109/ICoCS.2015.7483317.
V. Singh, V. K. Asari, and R. Rajasekaran, “A Deep Neural Network for Early Detection and Prediction of Chronic Kidney Disease,” Diagnostics, vol. 12, no. 1, pp. 1–22, 2022, doi: 10.3390/diagnostics12010116.
M. A. Islam, M. Z. H. Majumder, and M. A. Hussein, “Chronic kidney disease prediction based on machine learning algorithms,” J. Pathol. Inform., vol. 14, no. September 2022, p. 100189, 2023, doi: 10.1016/j.jpi.2023.100189.
D. A. Debal and T. M. Sitote, “Chronic kidney disease prediction using machine learning techniques,” J. Big Data, vol. 9, no. 1, 2022, doi: 10.1186/s40537-022-00657-5.
H. Li, Y. Pan, J. Zhao, and L. Zhang, “Skin disease diagnosis with deep learning: A review,” Neurocomputing, vol. 464, pp. 364–393, 2021, doi: 10.1016/j.neucom.2021.08.096.
R. Ahmad and B. K. Mohanty, “Biomedical Signal Processing and Control Chronic kidney disease stage identification using texture analysis of ultrasound images,” Biomed. Signal Process. Control, vol. 69, no. April, p. 102695, 2021, doi: 10.1016/j.bspc.2021.102695.
W. Tuntayothin, S. J. Kerr, C. Boonyakrai, S. Udomkarnjananun, S. Chukaew, and R. Sakulbumrungsil, “Development and Validation of a Chronic Kidney Disease Prediction Model for Type 2 Diabetes Mellitus in Thailand,” Value Heal. Reg. Issues, vol. 24, pp. 157–166, 2021, doi: 10.1016/j.vhri.2020.10.006.
C. Sabanayagam et al., “Articles A deep learning algorithm to detect chronic kidney disease from retinal photographs in community-based populations,” Lancet Digit. Heal., vol. 2, no. 6, pp. e295–e302, doi: 10.1016/S2589-7500(20)30063-7.
K. M. Almustafa, “Prediction of chronic kidney disease using different classification algorithms,” Informatics Med. Unlocked, vol. 24, p. 100631, 2021, doi: 10.1016/j.imu.2021.100631.