Abstract
Chronic kidney disease is one of the serious health care issues faced by people across the globe. It is majorly resulting in kidney failure or sometimes leads to cardiovascular disease, or sometimes leads to the death of a person. So, the detection of this disease in the early stages plays a significant role which helps in treating and controlling the disease. In this paper, various machine learning algorithms are demonstrated that disclose and extract hidden information from clinical and laboratory patient data, which can aid clinicians in maximizing accuracy for illness severity stage assessment. Several machine learning algorithms like KNN, RF, AdaBoost, gradient boost, and a voting classifier were considered, and a comparative study was done. These comparisons were made by taking the CKD dataset available in the UCI repository. The models employed for the study provide much accuracy, greater than prior research, suggesting that they are more trustworthy than the previous models.
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Swathi, K., Vamsi Krishna, G. (2023). Prediction of Chronic Kidney Disease with Various Machine Learning Techniques: A Comparative Study. In: Ogudo, K.A., Saha, S.K., Bhattacharyya, D. (eds) Smart Technologies in Data Science and Communication. Lecture Notes in Networks and Systems, vol 558. Springer, Singapore. https://doi.org/10.1007/978-981-19-6880-8_27
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DOI: https://doi.org/10.1007/978-981-19-6880-8_27
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