CHRONIC KIDNEY DISEASE PREDICTION INTEGRATING CATEGORIZATION AND NOVEL MACHINE LEARNING TECHNIQUES
DOI:
https://doi.org/10.48047/vs76ze78Keywords:
Chronic kidney disease (CKD), Machine Learning(ML), Ant Colony Stacked MultiTask Adaptive Naive Bayes (ACS-MANB), Genetic Fine-Tuned Regressive Support Vector Machine (GFR-SVM), kernel-based principal component analysis (K-PCA)Abstract
Chronic kidney disease (CKD), that causes undesired and rising volumes of hazardous liquids and
wastes to accumulate in the blood and damage the body, is a typical issue with kidney function.
An early screening tool to detect renal function is crucial in many circumstances because such
degradation of kidney function results in kidney damage and occasionally in death. In order to
diagnose and categorise diseases, Machine Learning(ML)approaches were widely applied in
healthcare.In first stage, we propose Ant Colony Stacked Multi-Task Adaptive Naive Bayes
(ACS-MANB) methodto efficiently predict the CKDs. Initially, the CKD dataset is collected for
this study. To eliminate the unwanted/duplicated information from the gathered data,
normalization approach is used.
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