• Harshada Krishnapuram
• Dr. A Satyanarayana
• Y.Sowjanya
• K.Uma

- Chronic Kidney Disease (CKD) has a widespread influence on the world's population and causes substantial numbers of fatalities every year due to kidney malfunction. The objective of this venture is to conduct a comprehensive analysis of the CKD data collection with the purpose of extracting meaningful information. By visualizing and evaluating the data, our aim is to bring to light the symptoms of CKD and determine the impact that different elements have on the ailment. This project details the steps taken for the formation of a data set, the preparation of a model, and the prediction of disease. The process begins with the procurement of structured and unstructured data from diverse sources, referred to as data collection. Following the collection, the data undergoes preprocessing and is partitioned into a cleaning data set and a test data set. The training data set is utilized to train machine learning algorithms such as the Random Forest, Gaussian Naive Bayes, and Gradient Boosting Classifier for multiple cycles, with the goal of enhancing the accuracy of prediction outcomes. Upon reaching the desired level of accuracy, the model is ready for testing.

Utilizing Hybrid Machine Learning Techniques for Chronic Kidney Disease Diagnosis

"Utilizing Hybrid Machine Learning Techniques for Chronic Kidney Disease Diagnosis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 3, page no.e411-e418, March-2023, Available :http://www.jetir.org/papers/JETIR2303450.pdf

"Utilizing Hybrid Machine Learning Techniques for Chronic Kidney Disease Diagnosis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 3, page no. ppe411-e418, March-2023, Available at : http://www.jetir.org/papers/JETIR2303450.pdf