Education, Science, Technology, Innovation and Life
Open Access
Sign In
  1. Home
  2. Journals
  3. Advances in Computer, Signals and Systems
  4. Vol 7, Issue 11, 2023

Tree-Based Prediction of Influential Factors and Information Mining

Download as PDF

DOI: 10.23977/acss.2023.071108 | Downloads: 6 | Views: 246

Author(s)

Xin Tan 1

Affiliation(s)

1 Hubei University of Education of Mathematics and Statistics, Wuhan, Hubei, 430205, China

Corresponding Author

Xin Tan

ABSTRACT

In minimally invasive gastrointestinal surgery (IPI), local sedative and analgesic drugs are required, and a new type of drug, "R-drug", has yet to be studied non-intervention ally. This paper analyzes and explores the vital signs, adverse effects and patient satisfaction of IPI based on the real performance data of new and traditional sedative drugs in clinical trials. In this paper, we first cleaned, coded and normalized the data, then based on multivariate visualization analysis, we found that there were significant differences between different drug groups regarding each adverse reaction, and we conducted chi-square test on different drug groups regarding each adverse reaction, and we found that there were significant differences between different drug groups regarding intra-operative adverse reactions, and only "nausea and vomiting" and "abdomen and vomiting" were found in the post-operative adverse reactions. Among the postoperative adverse reactions, only "nausea and vomiting" and "abdominal distension and abdominal pain" showed significant differences. Regarding the prediction of adverse reactions, this paper up-sampled the dataset and built a model based on the K nearest neighbor algorithm, and the classification AUC of the model on the tested dataset was above 0.92, and the confusion matrix and ROC diagram were made to visualize the specific testing of the model.

KEYWORDS

Traffic Flow; Large-scale Gathering Activities; Traffic Congestion; Macro Base Map; VISSIM Simulation

CITE THIS PAPER

Xin Tan, Tree-Based Prediction of Influential Factors and Information Mining. Advances in Computer, Signals and Systems (2023) Vol. 7: 49-54. DOI: http://dx.doi.org/10.23977/acss.2023.071108.

REFERENCES

[1] SHEN Qing, ZHANG Lianzeng. A new approach to bank credit risk identification: a combined SVM-KNN model[J]. Research on Financial Regulation, 2020, (07):23-37.
[2] Sarkar S , Vinay S , Maiti J .Text mining based safety risk assessment and prediction of occupational accidents in a steel plant[C]//International Conference on Computational Techniques in Information & Communication Technologies.IEEE, 2016.DOI:10.1109/ICCTICT.2016.7514621.
[3] Wang Dapeng, Yan Su, Wang Nan, et al. Analysis of intelligent fire protection industry based on chi-square test and rank sum test[J]. Fire Science and Technology, 2022, 41(11):1594.
[4] Haochen Wang, Changlun Zhang, Mingliang Lai. Research on deep learning based point cloud upsampling algorithm [J]. JournalofImageandSignalProcessing, 2023, 12:21.
[5] Cao Qian. Research on multispectral dimensionality reduction algorithm based on second-order polynomial regression and weighted principal component analysis[J].Optical Technique, 2023, 49(2):250-256.
[6] Quancheng Z , Jingbin H .Research on Data Mining of Physical Examination for Risk Factors of Chronic Diseases Based on Classification Decision Tree[J].  2021.DOI:10.1109/ICSP51882.2021.9408682.
[7] K. Zhang, K. Zhang. Research on slope stability prediction based on LightGBM algorithm[J]. Chinese Journal of Safety Science, 2022, 32(7):113.
[8] Yang Qiang, Feng Yan, Guan Li, Wu Wenyu, Wang Sichen, Li Qiangyu. XBand Radar Attenuation Correction Method Based on LightGBM Algorithm[J].RemoteSensing, 2023, 15(3).
[9] Lee S , Choi K , Yoo D .Predicting the Insolvency of SMEs Using Technological Feasibility Assessment Information and Data Mining Techniques[J].Sustainability, 2020, 12(23):9790.DOI:10.3390/su12239790.
[10] Harsha P , Manikanta V , Kumara S S ,et al.Classification and Regression Tree - Based Analysis for the Prediction and Mapping of Funding Pattern[J].Srels Journal of Information Management, 2012.
[11] Shuchen Wu, Zongfeng Qi, Jianxun Li. Intelligent global sensitivity analysis based on deep learning[J]. Journal of Shanghai Jiao Tong University, 2022, 56(7):840.
[12] Ren Jiadong, Liu Xinqian, Wang Qian, He Haitao, Zhao Xiaolin. A multi-layer intrusion detection method based on KNN outlier detection and random forest [J]. Computer Research and Development, 2019, 56(03):566-575.

Subscription
E-Mail Alert
Most popular papers
Call for Special Issue
Publication Ethics & OA Statement
Plagiarism Policy
Contact Us
Downloads: 13687
Visits: 260461

Sponsors, Associates, and Links

All published work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright © 2016 - 2031 Clausius Scientific Press Inc. All Rights Reserved.