Literature DB >> 30097310

Using Naive Bayes Classifier to predict osteonecrosis of the femoral head with cannulated screw fixation.

Shuangshuang Cui1, Likun Zhao1, Yumin Wang2, Qiang Dong2, Jianxiong Ma1, Ying Wang1, Wenjun Zhao1, Xinlong Ma3.   

Abstract

Predictive models permitting personalized prognostication for patients with cannulated screw fixation for the femoral neck fracture before operation are lacking. The objective of this study was to train, test, and cross-validate a Naive Bayes Classifier to predict the occurrence of postoperative osteonecrosis of cannulated screw fixation before the patient underwent the operation. The data for the classifier model were obtained from a ambispective cohort of 120 patients who had undergone closed reduction and cannulated screw fixation from January 2011 to June 2013. Three spatial displaced parameters of femoral neck: displacement of centre of femoral head, displacement of deepest of femoral head foveae and rotational displacement were measured from preoperative CT scans using a 3-dimensional software. The Naive Bayes Classifier was modelled with age, gender, side of fractures, mechanism of injury, preoperative traction, Pauwels angle and the three spatial parameters. After modelling, the ten-fold cross-validation method was used in this study to validate its performance. The ten-fold cross-validation method uses the whole dataset to be trained and tested by the given algorithm. Two of the three spatial parameters of femoral neck (displacement of center of femoral head and rotational displacement) were included successfully in the final Naive Bayes Classifier. The Classifier achieved good performance of the accuracy (74.4%), sensitivity (74.2%), specificity (75%), positive predictive value (92%), negative predictive value (42.9%) and AUC (0.746). We showed that the Naive Bayes Classifier have the potential utility to be used to predict the osteonecrosis of femoral head within 5 years after surgery. Although this study population was restricted to patients treated with cannulated screws fixation, Bayesian-derived models may be developed for application to patients with other surgical procedures at risk of osteonecrosis.
Copyright © 2018. Published by Elsevier Ltd.

Entities:  

Keywords:  Naive Bayes classifier; Osteonecrosis of femoral head; Predict; Spatial displacements

Mesh:

Year:  2018        PMID: 30097310     DOI: 10.1016/j.injury.2018.07.025

Source DB:  PubMed          Journal:  Injury        ISSN: 0020-1383            Impact factor:   2.586


  3 in total

1.  A Comparative Classification Analysis of Abdominal Aortic Aneurysms by Machine Learning Algorithms.

Authors:  Balaji Rengarajan; Wei Wu; Crystal Wiedner; Daijin Ko; Satish C Muluk; Mark K Eskandari; Prahlad G Menon; Ender A Finol
Journal:  Ann Biomed Eng       Date:  2020-01-24       Impact factor: 3.934

2.  A Machine Learning-Based Investigation of Gender-Specific Prognosis of Lung Cancers.

Authors:  Yueying Wang; Shuai Liu; Zhao Wang; Yusi Fan; Jingxuan Huang; Lan Huang; Zhijun Li; Xinwei Li; Mengdi Jin; Qiong Yu; Fengfeng Zhou
Journal:  Medicina (Kaunas)       Date:  2021-01-22       Impact factor: 2.430

3.  Prediction Model of Osteonecrosis of the Femoral Head After Femoral Neck Fracture: Machine Learning-Based Development and Validation Study.

Authors:  Huan Wang; Wei Wu; Chunxia Han; Jiaqi Zheng; Xinyu Cai; Shimin Chang; Junlong Shi; Nan Xu; Zisheng Ai
Journal:  JMIR Med Inform       Date:  2021-11-19
  3 in total

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