CHRONIC KIDNEY DISEASE PREDICTION USING MACHINE LEARNING
NANDAM VENKATA KUSUMA SAI1, KOTHURI GNANA SARASWATHI2, KUNCHALA REVANTH KUMAR3 , POTHALA RAJESH4 ,
Dr. N. GOPALA KRISHNA5
1,2,3,4 Student, Department of Computer Science and Engineering, Tirumala Engineering College
5 Professor, Department of Computer Science and Engineering, Tirumala Engineering College
---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - The term ―chronic kidney disease‖ means lasting damage to the kidneys that can get worse over time. If the damage is very bad, your kidneys may stop working. This is called kidney failure, or end-stage Kidney disease (ESRD). Kidney disease patients have the potential to get into the chronic phase and chronic kidney disease (CKD) is a decrease in kidney function gradually. So, doctors can diagnose kidney disease patients. So, predicting whether patients with Kidney disease have entered a phase of chronic kidney disease or not by showing the best accuracy result of comparing supervised classification machine learning algorithms. The aim is to investigate machine learning- based techniques for CKD forecasting by predicting results in the best accuracy. The analysis of the dataset by supervised machine learning technique (SMLT) to capture several information like, variable identification, univariate analysis, bi-variate and multi-variate analysis, missing value treatments and analyze the data validation, data cleaning/preparation, and data visualization will be done on the entire given dataset.
Additionally, to compare and discuss the performance of various machine learning algorithms from the given hospital dataset with an evaluation classification report, identify the confusion matrix and to categorizing data from priority and the result shows that the effectiveness of the proposed machine learning algorithm technique can be compared with the best accuracy with precision, Recall and F1 Score.
Key Words: Chronic kidney, Damage, Univariate, bi-variate, Multi-variate.
