CHRONIC KIDNEY DISEASE PREDICTIONS USING MACHINE LEARNING MODELS
DOI:
https://doi.org/10.61841/f6a55a76Abstract
When it comes to clinical disorders, chronic kidney disease (CKD) is an umbrella term that refers to a wide range of illnesses that deteriorate as kidney function degrades over time. It refers to a wide variety of medical conditions. The term "chronic renal failure" is sometimes used to describe this illness in some circles. Various factors, including genetic abnormalities in the kidneys and systemic illnesses that damage the kidneys, can contribute to chronic kidney disease. Depending on the underlying reason, it might express itself in a variety of ways. Worldwide, the number of people suffering from chronic kidney disease (CKD) is growing year after year, according to the World Health Organization. As defined by the World Health Organization, chronic kidney disease (CKD) is a worldwide public health concern with an increasing incidence and a vast geographic reach that affects individuals all over the world. GFR rises in the presence of renal failure needing dialysis, and it is widely regarded to be the most reliable overall indicator of kidney function in the general population. Heart disease (including high blood pressure and anaemia) and a variety of metabolic problems, to mention a few, are among the additional risk factors for kidney failure. Because of a statistical approach known as 10-fold cross-validation, the algorithms of logistic regression, support vector machines, random forest, and gradient boosting have all been trained and tested on real-world data. According on the F1measure gathered by the classifier after training, the accuracy of the Gradient Boosting classifier is 99.1 percent correct. In addition, we discovered that haemoglobin has a bigger significance for both random forest and gradient boosting in the diagnosis of chronic renal sickness than was previously believed to be the case, which is in direct opposition to previous notions.
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