SVM Computer Aided Diagnosis for Anesthetic Doctors
Mohammed El Amine Lazouni1, Mostafa El Habib Daho2, Nesma Settouti3, Mohammed Amine Chikh4

1Mohammed El Amine Lazouni, License Degree, Department of Electronic Biomedical, Tlemcen University, Algeria
2Mostafa El Habib Daho, Graduated, Master Degree, Department of Computer Sciences, Thesis Under Joint Supervision University of Tlemcen Algeria
3Nesma Settouti, Engineer Degree, Department of Electrical Biomedical, Tlemcen University, Algeria
4Mohamed Amine Chikh, Graduated, Department of Electrical Engineering, Institut INELEC of Boumerdes  Algeri.
Manuscript received on 15 April 2013 | Revised Manuscript received on 22 April 2013 | Manuscript Published on 30 April 2013 | PP: 235-240 | Volume-2 Issue-5, April 2013 | Retrieval Number: E0640032413/13©BEIESP
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Abstract: The application of machine learning tools has shown its advantages in medical aided decisions. The purpose of this study is to construct a medical decision support system based on support vector machines (SVM) with 30 physical features for helping the Doctors Specialized in Anesthesia (DSA) in pre-anesthetic DSA examination or preoperative consultation. For that, in this work, a new dataset has been obtained with the help of the DSA. The 898 patients in this database were selected from different private clinics and hospitals of western Algeria. The medical records collected from patients suffering from a variety of diseases ensure the generalization of the performance of the decision system. In this paper, the proposed system is composed of four parts where each one gives a different output. The first step is devoted to the automatic detection of some typical features corresponding to the American Society of Anesthesiologists scores (ASA scores). These characteristic are widely used by all DSA in pre-anesthetic examinations. In the second step, a decision making process is applied in order to accept or refuse the patient for surgery. The goal of the following step is to choose the best anesthetic technique for the patient, either general or local anesthesia. In the final step we examine if the patient’s tracheal intubation is easy or hard. Moreover, the robustness of the proposed system was examined using a 6-fold cross-validation method and the results show the SVM-based decision support system can achieve an average classification accuracy of 87.52% for the first module, 91.42% for the second module, 93.31% for the third module and finally 94.76 % for the fourth module.
Keywords: Doctors Specialized in Anesthesia, Support Vector Machines, American Society of Anesthesiologists Scores, Machine Learning, Pre-Anesthetic Examination.

Scope of the Article: Computer Network