###### Abstract  
[PERSON]'s disease is a neurodegenerative disease that is associated with genetic and environmental factors. However, the genes causing this degeneration have not been determined, and no reported cure exists for this disease. Recently, studies have been conducted to classify diseases with RNA-seq data using machine learning, and accurate diagnosis of diseases using machine learning is becoming an important task. In this study, we focus on how various feature selection methods can improve the performance of machine learning for accurate diagnosis of [PERSON]'s disease. In addition, we analyzed the performance metrics and computational costs of running the model with and without various feature selection methods. Experiments were conducted using RNA sequencing--a technique that analyzes the transcription profiling of organisms using next-generation sequencing. Genetic algorithms (GA), information gain (IG), and wolf search algorithm (WSA) were employed as feature selection methods. Machine learning algorithms--extreme gradient boosting (XGBoost), deep neural network (DNN), support vector machine (SVM), and decision tree (DT)--were used as classifiers. Further, the model was evaluated using performance indicators, such as accuracy, precision, recall, \(F_{1}\) score, and receiver operating characteristic (ROC) curve. For XGBoost and DNN, feature selection methods based on GA, IG, and WSA improved the performance of machine learning by 10.00% and 38.18%, respectively. For SVM and DT, performance was improved by 0.91% and 7.27%, respectively, with feature selection methods based on IG and WSA. The results demonstrate that various feature selection methods improve the performance of machine learning when classifying [PERSON]'s disease using RNA-seq data. [PERSON]'s disease; RNA sequences; genetic algorithm; information gain; wolf search algorithm; extreme gradient boosting; deep neural network; support vector machine; decision tree +
Footnote †: journal: Medical  
Many studies have conducted a metaheuristic-based feature selection method to improve the performance of machine learning. Metaheuristic algorithms are recognized as robust problem solvers to solve a variety of problems of different types--genetic algorithm [21; 22], particle swarm optimization [23], heat transfer search optimization [24], starling murmuration optimizer [25; 26], wolf search algorithm [27], and grey wolf optimization [28; 29] are some of the successful metaheuristic algorithms, which can be applied to feature selection for machine learning. The performance of feature selection methods is generally related with machine learning models such as extreme gradient boost (XGBoost), multilayer perceptron (MLP), deep neural networks (DNN), support vector machine (SVM), and decision trees (DT). In particular, tree-based ensemble models such as XGBoost showed outstanding performance on prediction and classification problems [30; 31; 32; 33]. For example, according to [PERSON] et al. [34], the combination of particle swarm optimization and the XGBoost model showed good performance. Research on classifying diseases by feature selection of RNA sequence (RNA-seq) has been steadily presented in the literature [35; 36; 37]. This study used data from 29 patients with [PERSON]'s and 44 RNA-seq recordings without Parkinson's [38]. RNA-seq data were classified using machine learning using various feature selection methods. There was only one paper [39] that used the same data as this study. In this previous work, a deep neural network called DEGnet was proposed to predict only UR and DR genes in [PERSON]'s disease, and the classification problem, which is investigated in this study, was not addressed. The machine learning models used in this study include extreme gradient boost (XGBoost), deep neural networks (DNN), support vector machine (SVM), and decision trees (DTs). Cross-validation was conducted to evaluate the performance of the model. Furthermore, the model was evaluated using receiver operating characteristic (ROC) curves.