# Explainable Machine Learning Model for Chronic Kidney Disease Prediction
## 2 Literature Review
The work did not address issues with explainability, HPO, or advanced ML approaches [30]. [PERSON] (2023) conducted a study on predicting CKD using ML methods. The study specifically utilized the UCI CKD dataset. Mean imputation was used for numerical variables and mode imputation for categorical variables to fix the missing data issue. The researchers utilized the SMOTE technique to balance the data and determined nine crucial features based on the chi-squared score. By employing RF and SVM methods, they fine-tuned hyperparameters using grid search CV, resulting in an accuracy of 99.33% for SVM and 98.67% for RF. The 10-fold CV score demonstrated the model's capacity to generalize. Nevertheless, applying SMOTE to the entire dataset instead of only the training set may induce biases. The study did not employ any explainable strategies to guarantee model transparency [31]. The study conducted by [PERSON] et al. (2023) introduced an ML model for predicting CKD. The model used a sequential data scaling technique that combined robust, standard, and min-max scaling, and iterative imputation was used to deal with missing values. The researchers used Gaussian NB and KNN models, optimizing hyperparameters by grid search CV. As a result, they attained a 100% accuracy rate with KNN and a 97.5% accuracy rate with Gaussian NB. Their model's generalization was validated through a 10-fold CV score. Yet, the absence of explainable techniques in their approach raises concerns about the model's transparency [32]. In 2023, numerous further research projects also made contributions to the prediction of CKD using ML approaches on the UCI CKD dataset. A study that was carried out by [PERSON] et al. showed that Light GBM outperformed other models in predicting CKD, with an impressive accuracy rate of 99% [33]. Furthermore, various ML classifiers were examined by [PERSON] et al., with the XgBoost classifier achieving the highest performance metrics at 98.3% [34]. [PERSON], in a similar vein, compared the efficacy of XgBoost with a variety of base learners, such as SVM, KNN, RF, logistic regression,and DTs. The findings demonstrated that XGBoost surpassed its competitors with an accuracy of 98.00% [35]. [PERSON] et al. conducted a comparative analysis of the performance of various boosting algorithms: XgBoost, CatBoost, LightGBM, AdaBoost, and GBM. AdaBoost demonstrated superior overall performance, attaining an accuracy of 98.47% [36]. [PERSON] et al. used various ML techniques like KNN, DTs, and artificial neural networks, finding that the DTs showed the best result with 98.60% accuracy [37]. All these studies have limitations, such as a lack of advanced preprocessing steps, advanced ML models, and explainable techniques for transparency. After going over this research, it's obvious that there are a lot of holes and restrictions that need to be filled in order to improve CKD prediction. This study aims to fill a gap in the existing literature by addressing these limitations and adding new perspectives. Our work primarily introduces the following innovations:  
1. Effective preprocessing steps are used to improve the quality of the dataset, including KNN imputation for numerical features and mode imputation for categorical features. 2. Feature selection is performed using the SelectKBest method with mutual info score to select the top 12 features, enhancing decision-making and reducing computation time. 3. For the purpose of detecting and predicting CKD, an MLP model is suggested. The model is trained using 75% of the dataset and validated using 25%. By using LIME, one may better understand the model's predictions and the reasoning behind them. 4. Several assessment metrics, including accuracy, precision, recall, F1-score, and curve analysis, are employed to confirm the efficacy of the proposed model. In addition, we evaluate the performance of the proposed model by comparing it to other models such as RF, DTs, KNN, ridge classifier, logistic regression, stochastic gradient descent (SGD), Bernoulli NB, and Gaussian NB.