# Explainable Machine Learning Model for Chronic Kidney Disease Prediction
## 2 Literature Review  
In recent years, ML has made remarkable progress in illness prediction and clinical decision-making in the CKD field. Several studies propose ML models for the detection of CKD and perform a review of the developments in this field. This literature review will summarize and analyze the latest research from 2023 and 2024. It will touch on their methodologies, results, and boundaries and offer a comprehensive assessment of the actual progress in this field--and where its research problems stand. [PERSON] et al. (2024) developed an ML-based CKD prediction model. The model's data preprocessing includes imputed missing data, min-max scaling, and categorical variable to numerical conversion. Feature selection methods such as lasso regression, ridge regression, sequential forward selection, variance cutoff, correlation analysis, and chi-square tests are used to refine the datasets. Various predictive models were employed to forecast CKD, including decision trees (DTs), random forests (RF), adaptive boosting (AdaBoost), support vector machines (SVM), extreme gradient boosting (XgBoost), naive bayes (NB), and gradient boosting machine (GBM). RF and AdaBoost achieve 100% accuracy in validation methods like 70:30, 80:20, and 10-fold. The study did not include explainability measures to increase model transparency [26]. [PERSON] et al. proposed the TrioNet Model for CKD in 2024, which takes an ensemble of extra tree classifiers, RF, and XgBoost as its base model. The K-nearest neighbors (KNN) imputation was employed by the researchers to fill in missing data, and they applied the synthetic minority over-sampling technique (SMOTE) for data balancing. This resultedin an accuracy of 98.97%. Two significant drawbacks of this study are that it does not use feature selection and hyperparameter optimization (HPO) techniques. Additionally, applying SMOTE to the entire dataset rather than just the training set introduced biases and constraints, and the study lacked explainability techniques to enhance model transparency [27]. A study by [PERSON] (2024) focused on CKD prediction using various ensemble learning classifiers, including AdaBoost, GBM, XgBoost, light GBM, RF, voting, stacking, and bagging. Multivariate imputation by chained equations was used to address missing data, and the borderline SVM-SMOTE method was employed for data balancing. Recursive feature elimination (RFE) and the Boruta method were employed to identify significant features, with RFE demonstrating superiority by selecting only 50% of the total features. Multiple performance metrics were utilized to identify the most effective classifiers for chronic kidney disease detection. Light GBM outperformed other models with the lowest compilation time and highest accuracy, achieving an average accuracy of 99.75%. However, like the different studies, this research applied the data balancing technique to the entire dataset, which might lead to biases, and did not integrate explainable techniques for model transparency [28]. [PERSON] et al. (2024) used a number of datasets to compare and contrast different ensemble ML methods for illness prediction. The researchers tested bagging, boosting, and stacking ensemble ML algorithms with different base classifiers. Using grid search HPO, ensemble approaches obtained 100% accuracy on the UCI CKD dataset. The study did not use sophisticated imputation methods to manage missing data, feature selection methods, or cross-validation (CV) to evaluate model generalization. Their methodology's absence of explainable procedures raises problems about model transparency and interoperability [29]. In their study, [PERSON] et al. (2023) sought to utilize the UCI CKD dataset to develop a ML model that can accurately identify CKD. In the analysis of missing data, the researchers employed [PERSON]'s MCAR test. For the purpose of feature selection, ant colony optimization was utilized. The classification was accomplished using DTs, RF, and KNN algorithms; the RF classifier yielded a 96% accuracy rate.