CHRONIC KIDNEY DISEASE PREDICTION USING GRADIENT BOOSTING AND KNN CLASSIFIER
Author(s):
O.RamaPraneeth Kumar, T.Naga Sampath, M.Lakshmi Narayana, N.Sai Prasad, N Md Jubair Basha
Keywords:
Chorionic Kidney Disease, Gradient Boosting, Support Vector machine (SVM), Random Forest (RF) and DecisionTree, K-Nearest Neighbor.
Abstract
Chronic kidney disease (CKD) is a global prevalent ailment that causes lives in a predominant number. Predictive analytics for healthcare using machine learning is a challenged task to help doctors decide the exact treatments for saving lives. Scientist researched collaboratively chronic kidney diseases, with the majority of their work on pure statistical models, generating numerous gaps in the development of machine-learning models. In this article we discussed the current methods and suggested improved technology based on the Gradient Boost, which combined significant characteristics of the F scores and evaluated four pre-processing scenarios. In addition, it is provided for machine training methods for anticipating chronic renal disease with Clinical information. Four techniques master Teaching are explored including Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting and Decision Tree, K-Nearest Neighbor. The components are made from UCI dataset of chronic kidney disease and the results of these models are compared to determine the best classification model for the prediction. From this four preprocessing cases, replacing missing values with mean values of each column and choosing important features was most logical as it allows to train with more data without dropping. However, Gradient Boosting gave the best outcomes in all four cases where it obtained 98% accuracy in case one where nulled valued are dropped, 98.75% testing accuracy for both case two and three where null values were replaced with minimum and maximum values of each column and it scores 100% accuracy in case four where null values are replaced with mean values. Thus, the system can be implemented for early stage CKD prediction in a cost efficient way which will be helpful for under developed and developingcountries.
Article Details
Unique Paper ID: 152994

Publication Volume & Issue: Volume 8, Issue 5

Page(s): 210 - 215
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