KLASIFIKASI NAIVE BAYES KEPARAHAN TRAUMA PASIEN MENGGUNAKAN DATA NEURO COGNITIVE DAN DATA PHYSIOLOGIC DENGAN PYTHON

(Naive Bayes Classification of Patient's Trauma Severity Using Neuro Cognitive Data and Physiologic Data with Python)

  • Puja Aditya Winata Universitas Negeri Semarang, Sekaran Gunungpati, Semarang

Abstract

Trauma is a mental health condition that can lead to post-traumatic stress disorder (PTSD). Several studies have shown that there are signs of PTSD in COVID survivors. This shows that the ongoing COVID-19 pandemic can increase PTSD patients. Therefore, it is important to diagnose the severity of trauma so that appropriate treatment can be carried out and to minimize the risk of increasing PTSD patients during the pandemic. The diagnosis of severity in this paper uses a machine learning classification method using the Naïve Bayes algorithm. The formation of this model is expected to help experts in correlating neuro-cognitive data and physiologic data to produce patient trauma diagnoses easily, quickly and precisely. The results of the accuracy using the confusion matrix obtained are 99.537%, so the formed model shows very good performance. 


Keywords: Classification, Naïve Bayes, Post-traumatic stress disorder, Trauma

Published
2022-08-14
How to Cite
WINATA, Puja Aditya. KLASIFIKASI NAIVE BAYES KEPARAHAN TRAUMA PASIEN MENGGUNAKAN DATA NEURO COGNITIVE DAN DATA PHYSIOLOGIC DENGAN PYTHON. UNEJ e-Proceeding, [S.l.], p. 99 - 108, aug. 2022. Available at: <https://jurnal.unej.ac.id/index.php/prosiding/article/view/33500>. Date accessed: 03 dec. 2022.