Currently, there are many people in the world suffering from chronic kidney diseases worldwide. Due to the several risk factors like food, environment and living standards many people get diseases suddenly without understanding of their condition. Chronic Kidney Disease prediction using Machine learning can help to get accurate data than traditional data analysis.
Diagnosing of chronic kidney diseases is generally invasive, costly, time-consuming and often risky. That is why many patients reach late stages of it without treatment, especially in those countries where the resources are limited.
Therefore, the early detection strategy of the kidney disease remains important, particularly in developing countries, where the diseases are generally diagnosed in late stages. Finding a solution for above-mentioned problems and riding out from disadvantages became a strong motive to conduct this study.
In this research study, the effects of using clinical features to classify patients with chronic kidney disease by using support vector machines algorithm is investigated. The chronic kidney disease dataset is based on clinical history, physical examinations, and laboratory tests.
Experimental results showed over 93% of success rate in classifying the patients with kidney diseases based on three performance metrics i.e., accuracy, sensitivity and specificity. Machine learning to predict end stage kidney disease Compared to traditional statistics, ML represents more sophisticated math functions and usually results in better performance.
The cost for providing care for patients on hem dialysis due to end stage kidney disease is high. Finding ways to improve patient outcomes and reduce the cost of dialysis is important. Dialysis care is intricate and multiple factors may in3uence patient survival.
Over 50 parameters may be monitored on a regular basis in providing kidney dialysis treatments. Understanding the collective role of these parameters in determining outcomes for an individual patient and administering individualized treatments allowing speci7c interventions is a challenge.
Individual patient survival may depend on a complex interrelationship between multiple demographic and clinical parameters, medications, medical interventions, and the dialysis treatment prescription.
In this research, data preprocessing, data transformations, and a data mining approach are used to elicit knowledge about the interaction between many of these measured parameters and patient survival.
System Architecture of Chronic Kidney Disease prediction
H/w and S/W requirements
Computer : System.
Ram : 1GB
Rom : 32GB
Technology : Machine Learning.
Front End : GUI-tkinter.
IDLE : python 3.10.4
Virtual Envs : Anaconda