Prediction of Minor Head Injured Patients Using Logistic Regression and MLP Neural Network

Authors:
Fatih S. Erol;Hadi Uysal;Uçman Ergün;Necaattin Barişçi;Selami Serhatholu;Firat Hardalaç
Affiliations:
Department of Neurosurgery, Faculty of Medicine, Firat University, Elazig, Turkey;Department of Radiology, Faculty of Medicine, Firat University, Elazig, Turkey;Department of Electronic and Computer Education, Faculty of Technical Education, Gazi University, Ankara, Turkey;Department of Electronic and Computer Education, Faculty of Technical Education, Gazi University, Ankara, Turkey;Department of Radiology, Faculty of Medicine, Firat University, Elazig, Turkey;Department of Biomedical, Faculty of Medicine, Gazi University, Ankara, Turkey
Venue:
Journal of Medical Systems
Year:
2005

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Abstract

In this study it is aimed to assess the posttraumatic cerebral hemodynamia in minor head injured patients. Eighty patients with minor head injury (Group 1) evaluated in the early 8 h of posttraumatic period between July 2003 and February 2004. The control group (Group 2) has composed of 32 healthy people. Bilateral blood flow velocities of middle cerebral arteries (MCA) had measured using transtemporal technique while internal carotid arteries were evaluated by submandibular examination. Two different mathematical models such as the traditional statistical method on the basis of logistic regression and a multi-layer perceptron (MLP) neural network are used to classify the age, sex, velocitiy parameters of MCA, mean velocity of extracranial ICAs and VMCA/ VICA ratios. The neural network was trained, cross-validated and tested with subject's transcranial Doppler signals. As a result of these classifications, we found the success rate of logistic regression, the success rate of MLP neural network is 88.2 and 89.1%, respectively. The classification results show that MLP neural network is offering the best results in the case of diagnosis.