## 3 Materials and Methods
### Boruta Algorithm for Feature Selection  
Feature selection is a crucial step in building predictive models, improving both performance and accuracy [29]. Datasets usually contain many features, but not all are equally important for prediction. Some features can unintentionally add noise or redundancy, leading to model overfitting and reduced generalization ability. This issue is especially prominent in transcriptome sequencing data, which is known for its high dimensionality.  
Among various feature selection methods, Boruta stands out [30]. Based on the Random Forest algorithm, Boruta excels in identifying and keeping all relevant features in a dataset while removing insignificant ones. Its effectiveness is not just theoretical; it has been proven in numerous medical datasets, as confirmed by several studies [31; 32; 33]. This evidence highlights Boruta's role in boosting predictive accuracy and strengthening model robustness, especially in complex, high-dimensional data settings.  
The Boruta algorithm has been adopted for feature selection. Rooted in the principles of the random forest methodology, Boruta assesses the import of original variables in contrast to a set of generated shadow features, facilitating the discernment of relevant variables from those deemed inconsequential. This algorithm exhibits robustness in handling diverse data typologies, including both categorical and numerical datasets, and is characterized by a reduced susceptibility to outlier influences. The utilization of Boruta contributes to the interpretability of the model, enabling a more profound comprehension of gene expression variables that are pivotal in the prediction of therapeutic efficacy. This approach is indicative of an in-depth appreciation of the complex and high-dimensional nature of genetic data, targeted towards the refinement of predictive accuracy in patient treatment outcomes. The steps of the Boruta algorithm are as follows:1. Extend the information system by adding shadow attributes created by shuffling original ones.
2. Train a random forest classifier on the extended information system and gather the importance measure (e.g., Mean Decrease Accuracy) for all attributes.
3. Find the maximum \(Z\) score among shadow attributes (MZSA) and then assign a significance level to each attribute where the importance measure for a given attribute is higher than MZSA.
4. Remove all attributes that are confirmed to be less relevant than shadow attributes, and iterate the above steps until all attributes are confirmed or rejected.  
The significance of original features is assessed using a \(Z\) test. The null hypothesis of this test is that a feature's importance is not greater than that of the shadow features. The \(Z\) score, calculated as follows, helps determine this:  
\[Z=\frac{M-\mu}{\sigma} \tag{1}\]  
Figure 1: Overview of the proposed method for bladder cancer treatment response prediction.  
where \(M\) is the original attribute's importance, \(\mu\) and \(\sigma\) are the mean and standard deviation of the shadow attributes' importance. Attributes with a \(Z\) score exceeding a predefined threshold, \(Z_{threshold}\), are considered relevant.