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Chronic Kidney Disease (CKD) has infected almost 800 million people aroundthe world. Around 1.7 million people die each year because of it. Detecting CKDin the initial stage is essential for saving millions of lives. Manyresearchers have applied distinct Machine Learning (ML) methods to detect CKDat an early stage, but detailed studies are still missing. We present astructured and thorough method for dealing with the complexities of medicaldata with optimal performance. Besides, this study will assist researchers inproducing clear ideas on the medical data preparation pipeline. In this paper,we applied KNN Imputation to impute missing values, Local Outlier Factor toremove outliers, SMOTE to handle data imbalance, K-stratified K-foldCross-validation to validate the ML models, and a novel hybrid featureselection method to remove redundant features. Applied algorithms in this studyare Support Vector Machine, Gaussian Naive Bayes, Decision Tree, Random Forest,Logistic Regression, K-Nearest Neighbor, Gradient Boosting, Adaptive Boosting,and Extreme Gradient Boosting. Finally, the Random Forest can detect CKD with100% accuracy without any data leakage.

Detecting Chronic Kidney Disease(CKD) at the Initial Stage: A Novel  Hybrid Feature-selection Method and Robust Data Preparation Pipeline for  Different ML Techniques