Evidence map›Paper›PMID 36028803›Full record

ArticleBMC medical imaging2022

Research on imbalance machine learning methods for MR

Xuanxuan Liu, Li Guo, Hexiang Wang, Jia Guo, Shifeng Yang, Lisha Duan

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC medical imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Xuanxuan LiuCollege of Computer Science and Technology, Qingdao University, Qingdao, 266071 China.
Li GuoCollege of Computer Science and Technology, Qingdao University, Qingdao, 266071 China.
Hexiang WangDepartment of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China.
Jia GuoDepartment of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China.
Shifeng YangDepartment of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.
Lisha DuanDepartment of Radiology, The Third Hospital of Hebei Medical University, Shijiazhuang, Qingdao, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSoft tissue sarcoma is a rare and highly heterogeneous tumor in clinical practice. Pathological grading of the soft tissue sarcoma is a key factor in patient prognosis and treatment planning while the clinical data of soft tissue sarcoma are imbalanced. In this paper, we propose an effective solution to find the optimal imbalance machine learning model for predicting the classification of soft tissue sarcoma data.

methodsIn this paper, a large number of features are first obtained based on

resultsThe experimental results show that the combination of extremely randomized trees (ERT) classification algorithm using SMOTETomek and the recursive feature elimination technique (RFE) performs best compared to other methods. The accuracy of RFE+STT+ERT is 81.57% , which is close to the accuracy of biopsy, and the accuracy is 95.69% when using another dataset splitting method.

conclusionPreoperative predicting pathological grade of soft tissue sarcoma in an accurate and noninvasive manner is essential. Our proposed machine learning method (RFE+STT+ERT) can make a positive contribution to solving the imbalanced data classification problem, which can favorably support the development of personalized treatment plans for soft tissue sarcoma patients.

Indexed as

Machine LearningSarcomaSoft Tissue NeoplasmsAlgorithmsHumansExtremely randomized treesImbalanced dataMachine learningRadiomicsSoft tissue sarcoma

Identifiers

PMID36028803
PMCPMC9417078

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.