## 5 Conclusions  
A neural-network-based method for detecting chronic kidney disease (CKD) has been successfully developed here. This prediction technique has a high level of accuracy and may be used by physicians as an alternative approach. It can also be used by ordinary people to determine the likelihood of developing CKD. Using a neural network, this study effort provides an efficient and cost-effective solution to the CKD-detection issue. In the study, by filling in the most likely values for all missing variables, the raw dataset is transformed into a highly preprocessed dataset. All the learning algorithms, optimized and traditional, were evaluated on the same dataset and on the heavily preprocessed data, where the optimized models, OCNN, OANN, and OLSTM, performed well. Based on prediction accuracy, OCNN was determined to be the best algorithm for the kidney dataset with an AUC score of 0.99 and a compilation time of 0.00447 s. From our overall work, we obtained the highest accuracy rate from OCNN, with 98.75%, followed by OLSTM and OANN, with 97% and 96.25%, respectively. Contrary to the high accuracy of the optimized models, the traditional models had unsatisfactory performance, with 92.71%, 90.43%, and 88.51%, for CNN, ANN, and LSTM, respectively. Our future research will concentrate on creating detection methods for additional illnesses and evaluating the effectiveness of neural networks on those diseases using various learning algorithms. Our primary goal is to enhance the detection system for illnesses, particularly chronic and severe disorders. In the future, we will work with merged datasets, where we will implement some deep learning and machine learning hybrid model. Due to having such complex architecture, the models tend to do overfitting. To handle this issue, we will use k-fold cross validation and feature-selection techniques. In our future work, variance results will be measured from the models, and we will check the validity of our proposed models on different datasets. In addition, we will also apply statistical significance testing on our proposed models. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/a15090308/s1](https://www.mdpi.com/article/10.3390/a15090308/s1)]([https://www.mdpi.com/article/10.3390/a15090308/s1](https://www.mdpi.com/article/10.3390/a15090308/s1)), Figure S1: Comparison of training accuracy of the models; Figure S2: Comparison of validation accuracy of the models; Figure S3: Comparison of training loss of the models; Figure S4: Comparison of validation loss of the models; Table S1: Changing convolution layer and dense layer are to evaluate the ablation study; Table S2: Changing the activation function to evaluate the ablation study; Table S3: Changing the dropout value to evaluate the ablation study; Table S4: Changing dense layers to evaluate the ablation study; Table S5: Changing the activation function to evaluate the ablation study; Table S6: Changing kernel initializer \begin{table}
\begin{tabular}{l l l l} \hline \hline  & Author & Model & Accuracy \\ \cline{2-4}  & [PERSON], et al. [6] & ANN & 88\% \\  & [PERSON], et al. [7] & EDL-CDSS & 96.91\% \\  & [PERSON], et al. [8] & Simple RNN & 96\% \\  & [PERSON], et al. [9] & DNN & 98\% \\  & [PERSON], et.al. [10] & ANN-SVM & 92.3\% \\  & [PERSON], et al. [11] & 1-D CorrNN-LSTM & 98.08\% \\  & [PERSON]. [PERSON], et al. [12] & CNN-SVM & 96.59\% \\  & [PERSON], et.al. [13] & SVM & 97.76\% \\ \hline \multirow{3}{*}{Our Work} & - & Optimized ANN & 96.25\% \\  & - & Optimized LSTM & 97\% \\ \cline{1-1}  & - & Optimized CNN & 98.75\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of proposed model with existing work.