Henry Gas Solubility Optimization Double Machine Learning Classifier for Neurosurgical Patients

This study aims to predict head trauma outcome for Neurosurgical patients in children, adults, and elderly people. As Machine Learning (ML) algorithms are helpful in healthcare field, a comparative study of various ML techniques is developed. Several algorithms are utilized such as k-nearest neighbor, Random Forest (RF), C4.5, Artificial Neural Network, and Support Vector Machine (SVM). Their performance is assessed using anonymous patients\u27 data. Then, a proposed double classifier based on Henry Gas Solubility Optimization (HGSO) is developed with Aquila optimizer (AQO). It is implemented for feature selection to classify patients\u27 outcome status into four states. Those are mortality, morbidity, improved, or the same. The double classifiers are evaluated via various performance metrics including recall, precision, F-measure, accuracy, and sensitivity. Another contribution of this research is the original use of hybrid technique based on RF-SVM and HGSO to predict patient outcome status with high accuracy. It determines outcome status relationship with age and mode of trauma. The algorithm is tested on more than 1000 anonymous patients\u27 data taken from a Neurosurgical unit of Mansoura International Hospital, Egypt. Experimental results show that the proposed method has the highest accuracy of 99.2% (with population size = 30) compared with other classifiers

Voices Raised, Issue 06

Included in this issue: Immaculate Mary; Grants augment women’s research; Mentoring grows; Women’s Studies take root in the neighborhood; Solution-oriented VP to retire; Muslim students strive to educate, support; Don’t let stress ruin your holidays; Dining services dishes up more than you’d expect; Marianist Images Across Campus; Confronting Disrespect: We Owe it to Each Other.https://ecommons.udayton.edu/wc_newsletter/1005/thumbnail.jp