## 5 Conclusions  
Feature selection for high-dimensional data aims to maximize prediction accuracy by identifying the smallest possible subset of features. However, traditional methods suffer from drawbacks such as excessive parameter adjustments and significant variations in results among different classifiers. In this study, causality is introduced into the domain of feature selection. The FSNT algorithm is employed to identify causal relationships among features in high-dimensional datasets, constructing a causality diagram to guide the selection of features based on their causal strength. Three distinct feature selection algorithms, namely CSFFS, XGBFS, and RFHFS, are chosen, with the absence of a feature selection algorithm serving as the Baseline. The study employs six real-world datasets with varying sizes and domains, encompassing eight classification algorithms and ten regression algorithms. The results indicate that the FSNT algorithm effectively eliminates redundant features in the three classification datasets and demonstrates superior overall classification performance compared to other feature selection algorithms. Among the three datasets, two datasets exhibit notable advantages, and the RFHFS algorithm emerges as the sole competitive algorithm to the FSNT algorithm. Across the three regression datasets, the FSNT algorithm performs exceptionally well and demonstrates clear advantages in regression evaluation for all datasets. The FSNT algorithm exhibits an average precision value improvement of 82.03% compared to other algorithms and achieves a significant reduction in the RMSE value. These results suggest that the FSNT algorithm is highly suitable for regression datasets. Extensive experiments have demonstrated the superior performance of the FSNT compared to other mainstream feature selection algorithms. However, it is observed that the running speed of the algorithm decreases when there is a large number of samples. Consequently, future research will focus on algorithm optimization and improving running speed to propose a more effective feature selection method. Conceptualization, J.L. ; Methodology, J.L. ; Validation, J.L. ; Formal analysis, J.L. ; Investigation, J.L. ; Resources, J.L. ; Data curation, J.L. ; Writing--review & editing, G.Z. ; Supervision, G.Z. ; Project administration, H.W. All authors have read and agreed to the published version of the manuscript. This work was supported by the National Natural Science Foundation of China (Grant Nos. 61263023 and 61863016). Not applicable. Data Availability Statement: Data is presented in the article. The authors declare no conflict of interest. * [PERSON] et al. (2021) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Early Prediction of Student's Performance in Higher Education: A Case Study. In _Trends and Applications in Information Systems and Technologies: Volume 1 9_; Springer International Publishing: Berlin/Heidelberg, Germany, 2021; pp. 166-175. * [PERSON] (2018) [PERSON] A data-driven statistical model for predicting the critical temperature of a superconductor. _Comput. Mater. Sci._**2018**, _154_, 346-354. [CrossRef]
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