Abstract
Chronic Kidney Disease (CKD) is a major cause of illness and death worldwide, with over 2 million cases diagnosed in the U.K. and potentially up to 1.8 million undiagnosed. However, there is a lack of longitudinal studies on CKD in India, resulting in limited data on its prevalence. CKD is often asymptomatic until 70% of the kidneys are severely damaged, and once this occurs, there is no cure. Patients may require dialysis or a kidney transplant to survive. Detecting the risk of CKD early is therefore crucial. In developing countries like India, many people cannot afford regular laboratory blood tests. This study aims to develop machine learning models to predict the likelihood of CKD using limited blood test results collected in India, including blood pressure, albumin, red and white blood cell count, blood urea, serum creatinine, HbA1Cs, and other biomarkers. Decision Trees and Logistic Regression classification algorithms were used, with hyperparameter tuning, achieving an F-score of 1. These promising results suggest that state-of-the-art results may be achievable with just six laboratory tests.
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References
Faulkner, S.L., Trotter, S.P.: Data saturation. In: Matthes, J., Davis, C.S., Potter, R.F. (eds.) The International Encyclopedia of Communication Research Methods (2017). https://doi.org/10.1002/9781118901731.iecrm0060
Dalrymple, L.S., et al.: Chronic kidney disease and the risk of end-stage renal disease versus death. J. Gen. Intern. Med. 26(4), 379–85 (2011)
Rule, A.D., Larson, T.S., Bergstralh, E.J., Slezak, J.M., Jacobsen, S.J., Cosio, F.G.: Using serum creatinine to estimate glomerular filtration rate: accuracy in good health and in chronic kidney disease. Ann. Int. Med. 141(12), 929–937 (2004). https://doi.org/10.7326/0003-4819-141-12-200412210-00009. PMID: 15611490
Witten, I.H., Frank, E., Hall, M.A.: Data Mining: Practical Machine Learning Tools and Techniques, 3rd edn. Morgan Kaufmann Publishers Inc., San Francisco, CA (2011)
Reddy, M., Cho, J.: Detecting chronic kidney disease using machine learning. In: Qatar Foundation Annual Research Conference Proceedings 2016 (ICTSP1534) (2016). https://doi.org/10.5339/qfarc.2016.ICTSP1534
Bhattacharya, M., Jurkovitz, C., Shatkay, H.: Assessing chronic kidney disease from office visit records using hierarchical meta-classification of an imbalanced dataset. In: 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 663–670. IEEE (2017)
Tirunagari, S., Bull, S.C., Vehtari, A., Farmer, C., De Lusignan, S., Poh, N.: Automatic detection of acute kidney injury episodes from primary care data. In: 2016 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1–6. IEEE (2016)
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Chennareddy, V.S.R., Tirunagari, S., Mohan, S., Windridge, D., Balla, Y. (2023). Interpretable Chronic Kidney Disease Risk Prediction from Clinical Data Using Machine Learning. In: Morusupalli, R., Dandibhotla, T.S., Atluri, V.V., Windridge, D., Lingras, P., Komati, V.R. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2023. Lecture Notes in Computer Science(), vol 14078. Springer, Cham. https://doi.org/10.1007/978-3-031-36402-0_63
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DOI: https://doi.org/10.1007/978-3-031-36402-0_63
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