Abstract
The medical diagnosis process can be interpreted as a decision making process, which doctors determine whether a person is suffering from a disease based on the medical examination. This process can also be computerized in order to present medical diagnostic procedures in an accurate, objective, rational, and fast way. This paper presents a detection model for small for gestational age (SGA) based on support vector machine (SVM). For this purpose, a dataset was adopted from pregnancy eugenic investigation to train the classification model. Then empirical experiments were conducted for SGA detection. The results indicate that support vector machine is considerably effective to detect SGA to help doctors make the final diagnosis.
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Acknowledgments
This work is supported by Beijing Natural Science Foundation (4152007), China National Key Technology Research and Development Program project with no. 2013BAH19F01 and Guangdong Key Laboratory of Popular High Performance Computers, Shenzhen Key Laboratory of Service Computing and Applications.
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Mo, H. et al. (2016). SVM Based Predictive Model for SGA Detection. In: Chang, C., Chiari, L., Cao, Y., Jin, H., Mokhtari, M., Aloulou, H. (eds) Inclusive Smart Cities and Digital Health. ICOST 2016. Lecture Notes in Computer Science(), vol 9677. Springer, Cham. https://doi.org/10.1007/978-3-319-39601-9_6
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DOI: https://doi.org/10.1007/978-3-319-39601-9_6
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