Kidney Disease Detection using Machine Learning
Authors
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Department of Computer Science & Engineering Swami Vivekananda University Kolkata, India. ,IN
Dibakar Dasgupta -
Department of Computer Science & Engineering Swami Vivekananda University Kolkata, India. ,INSubhojit Mukherjee
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Department of Computer Science & Engineering Swami Vivekananda University Kolkata, India. ,INAmit Chakraborty
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Department of Computer Science & Engineering Swami Vivekananda University Kolkata, India. ,INMou Majhi
DOI:
https://doi.org/10.18311/jmmf/2023/34162Keywords:
Machine Learning, Kidney Disease Detection, Feature selection, Classification, Gradient Boosting.Abstract
Different organs present in the human body which performed the relevant work. Kidney is one of the important body components that purifies the blood by removing toxic elements from the body. This is the reason, the kidney plays one of the important roles in the human body. Thus, the kidney needs to be safe in order to keep the body healthy. Different reasons are therefore which kidney is affected by a different disease. It can be seen that the reason behind kidney disease may differ for different persons. In this research, machine learning has been applied to the kidney disease dataset (collected from Kaggle) for the identification of kidney disease. Primarily, the analysis of the data has been conducted followed by the selection of essential attributes of the data so that the symptoms can be identified. In this scenario, the correlation mechanism has been employed to select essential attributes of the data. Using those essential attributes., the data has been finalized based upon which the detection of kidney disease has been commenced with the implication of classifiers of machine learning. To determine the best-performing model, the effectivenesses of the applied models have been done from where it has been identified that Gradient Boosting has performed the best with an accuracy rate of 99.62%.
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