Title: A Comparative Study of Supervised Machine Learning and Deep Learning Techniques with Feature Selection Methods for Classifying Parkinson’s Disease Based on Speech Impairments
Abstract: This paper compares and analyzes machine learning (ML) and deep learning (DL) techniques with different feature selection methods for classifying Parkinson’s disease based on a speech impairments dataset. The dataset was explored by various methods to obtain vocal features, including Mel Frequency Cepstral Coefficients (MFCCs), Wavelet Transform Features, Vocal Fold Features, and Tunable Q-factor wavelet transform (TQWT). It consists of 754 features and 756 instances. In addition, the study evaluates various classification techniques, including Support Vector Machine (SVM), Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), K-Nearest Neighbor (KNN), Naive Bayes (NB), Deep Neural Network (DNN), 1D Convolution Neural Network (1D CNN). Additionally, we demonstrated the effectiveness of feature selection techniques, namely Principal Component Analysis (PCA), Minimum Redundancy Maximum Relevance (mRMR), and SelectKBest. Among these, the most notable accuracy and precision were achieved by the KNN algorithm combined with PCA, recording values of 93.43% and 93.45%, respectively. Moreover, the Random Forest (RF) algorithm achieved an outstanding recall rate of 100%
Keywords: Parkinson’s disease;speech impairment;machine learning;deep learning;feature selection;dysphonia;detection disease
Review score: Moderate
Review comment: The study offers an insightful comparison of machine learning techniques for classifying Parkinson’s Disease based on speech impairments, marking a valuable contribution to the field. The exploration of various feature selection methods enhances the novelty. Nonetheless, to amplify the paper's impact, it would be beneficial to further detail the comparative analysis and explore additional deep learning approaches for future work. The research is aligned with conference themes, presenting a well-structured and methodologically sound study. A more detailed description of the dataset preparation, normalization methods, and the rationale behind choosing specific preprocessing techniques could enhance the reproducibility of the research and its applicability to similar studies.
The language used is technical and appropriate for the subject matter, demonstrating familiarity with the field's terminology and concepts. 
The paper's originality lies in its comprehensive comparison of multiple machine learning algorithms and feature selection methods specifically applied to speech impairments associated with Parkinson’s Disease. This focus on speech as a diagnostic tool for PD is timely and relevant, given the increasing interest in developing non-invasive and cost-effective methods for early disease diagnosis.
The paper leans more towards the experimental side of research. While it establishes a theoretical foundation by reviewing various machine learning algorithms and feature selection methods, its core contribution is the experimental evaluation and comparison of these methods.

Recommendations to the authors for improving their paper
1.	Given the rapid advancements in deep learning, comparing the performance of traditional machine learning models with deep learning models on the same tasks could provide valuable insights into their respective strengths and weaknesses in this application area. 
2.	Add the reference to Figure 1.