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Related Concept Videos

Higher Mental Functions of the Brain: Language01:10

Higher Mental Functions of the Brain: Language

Language is a system of communication that allows the expression of thoughts, ideas, and feelings. The brain processes language in both hemispheres.
Language formation and comprehension take place in the dominant hemisphere. The dominant hemisphere is responsible for understanding the meaning of spoken, written, or sign language, as well as the ability to communicate. For most people, the left hemisphere is the dominant one. The right hemisphere, then, gives tone and emotional context to the...
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Related Experiment Video

Updated: Jun 18, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

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Comparison of Predictive Models for Keloid Recurrence Based on Machine Learning.

Yan Hao1, Mengjie Shan1, Hao Liu1

  • 1Department of Plastic and Cosmetic Surgery, Peking Union Medical College Hospital, Beijing, China.

Journal of Cosmetic Dermatology
|February 7, 2025
PubMed
Summary
This summary is machine-generated.

Machine learning models predict keloid recurrence. The logistic regression model showed the best prognostic performance based on the area under the ROC curve (AUC), indicating its effectiveness in predicting keloid recurrence.

Keywords:
keloidmachine learningprediction modelrecurrencerisk factors

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Area of Science:

  • Dermatology
  • Medical Informatics
  • Biostatistics

Background:

  • Keloid recurrence after treatment poses a significant clinical challenge.
  • Accurate prediction of keloid recurrence is crucial for optimizing patient management and improving outcomes.

Purpose of the Study:

  • To develop and compare three machine learning models for predicting keloid recurrence.
  • To identify key factors influencing keloid recurrence.
  • To evaluate the predictive performance of logistic regression, decision tree, and random forest models.

Main Methods:

  • 301 keloid patients undergoing surgery and radiotherapy were included.
  • Models were trained on 70% of data and validated on 30%.
  • Performance was assessed using accuracy, sensitivity, specificity, precision, recall, kappa coefficient, and AUC.

Main Results:

  • Machine learning models identified KAAS, mean arterial pressure, postoperative complications, and inflammatory cell proportion as key predictors.
  • The decision tree model achieved the highest accuracy and precision.
  • The logistic regression model demonstrated the best performance in terms of AUC.

Conclusions:

  • Three machine learning models for keloid recurrence prediction were successfully established.
  • KAAS, blood pressure, postoperative complications, and inflammatory cell proportion are significant factors.
  • Logistic regression model offers the most favorable prognostic performance based on AUC.