Venue: ICLR 2023 TinyPapers

Reviews:

final_score: rating: Clear, Correct, and Reproducible (CCR): a submission which meets the reviewing criteria
review_content: summary_of_contributions: The authors present an analysis of acoustic-linguistic features that can be used to predict Mini Mental Status Exam scores used for Alzheimer detection. Using SHAP values to explain the prediction of LGBM model they find that the ARI value best contributes to model's prediction of MMSE scores. 
strengths_and_weaknesses: Strengths
- The authors propose a novel methodology to identify salient features that can be used for Alzheimer's detection.
- The paper is well written and the proposed methodology can be can be reproduced.

Challenges
- There may be a risk of authors uncovering an artifact specific to the lightGBM model as opposed to a globally salient feature that best predicts MMSE. The methodology exhibits a selection bias as only the best-performing model is selected for analysis.
- The authors should consider extending the Shapley values to other models and see if the AIR is consistently correlated with MMSE predictions. 
suggested_changes: n/a
review_title: null

final_score: rating: Great Start (GS): a submission which meets some of the reviewing criteria but has room for improvement
review_content: summary_of_contributions: The study employs machine learning models, to forecast Mini-Mental State Exam (MMSE) scores for Alzheimer's Disease detection. The authors then use the SHapley Additive exPlanations (SHAP) method to analyze feature importance and interpret the models' performance. The analysis reveals that the Automated Readability Index (ARI) is the most influential feature in predicting MMSE scores.
strengths_and_weaknesses: The authors proposed an interesting and valid problem. The study is well-organized, well-written, and addresses an important research gap in the literature. However, there are several points that need improvement:

Strengths:
1. The authors have a solid problem setting: determining the most important feature in Alzheimer's disease detection.
2. The authors conducted a sufficient literature review.
3. The authors discussed the result, stating that the Automated Readability Index (ARI) is the most influential feature, within a biological context.

Weakness:
1. The study is an incremental contribution to the problem.
2. The authors only used one comparison criterion for feature importance.
3. The features chosen for selection originate from a paper with the worst performance in comparison.
suggested_changes: 1. The authors could provide more details about the feature selection process employed by Aryal et al. and the rationale behind their choices.
2. As all machine learning models tested by the authors outperform the method in the Aryal et al. paper, it may not be reasonable to use the features selected by Aryal et al.
3. A more thorough comparison would involve discussing the pros and cons of using SHAP values for feature importance analysis.
4. There are many other criteria for feature importance, such as recursive feature elimination (RFE). It would be better to compare the SHAP values to those.
review_title: null

Decision: Invite to present