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Orthopedic disease classification based on breadth-first search algorithm.

Ahmed M Elshewey1, Ahmed M Osman2

  • 1Department of Computer Science, Faculty of Computers and Information, Suez University, P.O.Box:43221, Suez, Egypt.

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|October 7, 2024
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Summary
This summary is machine-generated.

This study enhances orthopedic disease diagnosis by optimizing machine learning models. Binary breadth-first search (BBFS) combined with Random Forest (RF) achieved 99.41% accuracy, significantly improving diagnostic capabilities.

Keywords:
HealthcareHybrid RF model, BFS-RFMLOrthopedic disease classificationOrthopedics

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Area of Science:

  • Orthopedics
  • Medical Diagnostics
  • Machine Learning

Background:

  • Orthopedic diseases are prevalent globally, affecting the musculoskeletal system and leading to pain and functional impairment.
  • Current diagnostic methods for orthopedic conditions may lack sufficient supplementary tools.
  • There is a need for improved and accurate methods for diagnosing orthopedic diseases.

Purpose of the Study:

  • To address the limitations in supplementary diagnostics for orthopedic diseases.
  • To enhance the methodology for diagnosing orthopedic diseases using advanced computational techniques.
  • To evaluate the efficacy of various feature selection and machine learning models in orthopedic diagnosis.

Main Methods:

  • Feature selection was performed using binary breadth-first search (BBFS), binary particle swarm optimization (BPSO), binary grey wolf optimizer (BGWO), and binary whale optimization algorithm (BWAO).
  • Six machine learning models, including Random Forest (RF), Support Grained Descent (SGD), Naive Bayes Classifier (NBC), Discriminant Analysis (DC), Quadratic Discriminant Analysis (QDA), and Extra Trees (ET), were applied.
  • The RF model's parameters were further optimized using BBFS, BPSO, BGWO, and BWAO, with performance evaluated using accuracy, sensitivity, specificity, F-score, and AUC curve.

Main Results:

  • BBFS demonstrated an average error reduction of 47.29% compared to other feature selection algorithms.
  • The Random Forest (RF) model initially achieved an accuracy of 91.4% among the tested machine learning models.
  • The optimized BBFS-RF model significantly improved performance, reaching an accuracy of 99.41% on the dataset.

Conclusions:

  • The integration of BBFS for feature selection and RF for classification offers a highly accurate approach to diagnosing orthopedic diseases.
  • Optimized machine learning models, particularly BBFS-RF, show substantial promise in enhancing the accuracy and reliability of orthopedic diagnostics.
  • This study provides a robust computational framework that can potentially improve patient outcomes through earlier and more precise disease detection.