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Simulator Training for Endovascular Neurosurgery

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Henry gas solubility optimization double machine learning classifier for neurosurgical patients.

Diana T Mosa¹, Amena Mahmoud², John Zaki³

¹Department of Information Systems, Faculty of Computers and Information, Kafrelsheikh University, Kafr El-Shaikh, Egypt.

|May 11, 2023

Summary

This study predicts head trauma outcomes using advanced machine learning. A novel hybrid model achieved 99.2% accuracy in classifying patient status, aiding neurosurgical care.

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Area of Science:

Neurosurgery
Medical Informatics
Artificial Intelligence

Background:

Head trauma significantly impacts patient outcomes across all age groups.
Accurate prediction of head trauma prognosis is crucial for effective neurosurgical patient management.
Machine learning (ML) offers potential for improving outcome prediction in neurosurgery.

Purpose of the Study:

To compare various ML algorithms for predicting head trauma patient outcomes.
To develop and evaluate a novel hybrid ML model for enhanced outcome prediction.
To investigate the relationship between patient age, trauma mode, and outcome.

Main Methods:

Comparative analysis of k-nearest neighbor, Random Forest (RF), C4.5, Artificial Neural Network, and Support Vector Machine (SVM).
Development of a double classifier using Henry Gas Solubility Optimization (HGSO) and Aquila Optimizer (AQO) for feature selection.
Implementation of a hybrid RF-SVM model integrated with HGSO for outcome classification into mortality, morbidity, improved, or same status.

Main Results:

The proposed hybrid RF-SVM and HGSO model achieved a high accuracy of 99.2%.
The model demonstrated superior performance compared to individual ML classifiers.
The study identified relationships between age, mode of trauma, and patient outcome.

Conclusions:

The developed hybrid ML model significantly enhances the accuracy of head trauma outcome prediction in neurosurgical patients.
This approach offers a valuable tool for clinicians to manage neurosurgical care more effectively.
The findings support the use of advanced ML techniques in personalized neurosurgical treatment strategies.