Abstract：Due to inconspicuous clinical characteristics of placenta accreta, there is no prenatal diagnosis methods with high sensitivity and specificity in clinical medicine. In this paper, feature selection methodis introduced into the prenatal diagnosis of placenta accreta. From the view of feature correlation, a multi-objective feature optimization problem is formulated to extract features with effective medical meaning for the prenatal diagnosis of placenta accreta, and then an improved non-dominated sorting genetic algorithm II (NSGA-II) is described to solve this problem. The computational result based on real clinical data for placenta accreta shows that the proposed method can extract placenta accreta features with effective medical meaning from complex clinical data of placenta accreta. The analysis based on receiver operating characteristic (ROC) curve shows that medical meaning of the extracted features has high diagnostic values, and it can be an effective decision tool for obstetricians to study the pathogenesis of placenta accreta and to make a timely prenatal diagnosis. The study reveals that some biochemistry characteristics in real diagnosis are very important and it can provide a reliable criterion for the prenatal diagnosis of placenta accreta.
潘晓晓，叶东毅，颜建英，张栋，杨丹林. 面向胎盘植入产前诊断的医学语义特征提取算法*[J]. 模式识别与人工智能, 2015, 28(6): 481-489.
PAN Xiao-Xiao , YE Dong-Yi , YAN Jian-Ying , ZHANG Dong , YANG Dan-Lin. Algorithm for Feature Extraction with Effective Medical Meaning for the Prenatal Diagnosis of Placenta Accreta. , 2015, 28(6): 481-489.
[1] Chen D J. Placenta Accreta. 1st Edition. Changsha, China: Hunan Science & Technology Press, 2013 (in Chinese) (陈敦金.胎盘植入.第1版.长沙:湖南科学技术出版社, 2013) [2] Baughman W C, Corteville J E, Shah R R. Placenta Accreta: Spectrum of US and MR Imaging Findings. RadioGraphics, 2008, 28(7): 1905-1916 [3] Lax A, Prince M R, Mennitt K W, et al. The Value of Specific MRI Features in the Evaluation of Suspected Placental Invasion. Magnetic Resonance Imaging, 2007, 25(1): 87-93 [4] Derman A Y, Nikac V, Haberman S, et al. MRI of Placenta Accreta: A New Imaging Perspective. American Journal of Roentgenology, 2011, 197(6): 1514-1521 [5] Liang N. MRI Research Progress in Placent Aincreta. Journal of Practical Radiology, 2013, 29(2): 315-318 (in Chinese) (梁 娜.MRI 在胎盘植入中的研究进展.实用放射学杂志, 2013, 29(2): 315-318) [6] Pang X S. A Comparative Study of Interpolation Processing Method for Missing Data. Statistics and Decision, 2012, (24): 18-22 (in Chinese) (庞新生.缺失数据插补处理方法的比较研究.统计与决策, 2012, (24): 18-22) [7] Fayyad U M, Irani K B. Multi-interval Discretization of Continuous-Valued Attributes for Classification Learning // Proc of the 13th International Joint Conference on Artificial Intelligence. Chambery, France, 1993, II: 1022-1027 [8] Ding C, Peng H C. Minimum Redundancy Feature Selection from Microarray Gene Expression Data. Journal of Bioinformatics and Computational Biology, 2005, 3(2): 185-205 [9] Peng H C, Long F H, Ding C. Feature Selection Based on Mutual Information: Criteria of Max-Dependency, Max-Relevance, and Min-Redundancy. IEEE Trans on Pattern Analysis and Machine Intelligence, 2005, 27(8): 1226-1238 [10] Kira K, Rendell L A. The Feature Selection Problem: Traditional Methods and a New Algorithm // Proc of the 10th National Confe-rence on Artificial Intelligence. San Jose, USA, 1992: 129-134 [11] Cai H M, Ruan P Y, Ng M, et al. Feature Weight Estimation for Gene Selection: A Local Hyperlinear Learning Approach[EB/OL]. [2014-03-15]. http://www.biomedcentral.com/content/pdf/1471-2105-15-70.pdf [12] Jia J H, Yang N, Zhang C, et al. Object-Oriented Feature Selection of High Spatial Resolution Images Using an Improved Relief Algorithm. Mathematical and Computer Modelling, 2013, 58(3/4): 619-626 [13] Liu H, Yu L. Toward Integrating Feature Selection Algorithms for Classification and Clustering. IEEE Trans on Knowledge and Data Engineering, 2005, 17(4): 491-502 [14] Ferreira A J, Figueiredo M A T. Efficient Feature Selection Filters for High-Dimensional Data. Pattern Recognition Letters, 2012, 33(13): 1794-1804 [15] Dai K, Yu H Y, Li Q. A Multi-class Feature Selection Algorithm Based on Support Vector Machine. Pattern Recognition and Artificial Intelligence, 2014, 27(5): 463-471 (in Chinese) (代 琨,于宏毅,李 青.一种基于支持向量机的特征选择算法.模式识别与人工智能, 2014, 27(5): 463-471) [16] Gong M G, Jiao L C, Yang D D, et al. Research on Evolutionary Multi-objective Optimization Algorithms. Journal of Software, 2009, 20(2): 271-289 (in Chinese) (公茂果,焦李成,杨咚咚,等.进化多目标优化算法研究.软件学报, 2009, 20(2): 271-289) [17] Srinivas N, Deb K. Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms. Evolutionary Computation, 1994, 2(3): 221-248 [18] Deb K, Pratap A, Agarwal S, et al. A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE Trans on Evolutionary Computation, 2002, 6(2): 182-197 [19] Guyon I, Elisseeff A. An Introduction to Variable and Feature Selection. Journal of Machine Learning Research, 2003, 3: 1157-1182 [20] Chang C C, Lin C J. LIBSVM: A Library for Support Vector Machines[EB/OL]. [2014-02-25]. http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.pdf [21] Hsu C W, Chang C C, Lin C J. A Practical Guide to Support Vector Classification[EB/OL]. [2014-02-25]. http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf [22] Liu L, Liu W Y, Chu C Y, et al. Classification of Tumid Lymph Nodes Metastases and Non-metastases from Lung Cancer in CT Image. Journal of Electronics & Information Technology, 2009, 31 (10): 2476-2482 (in Chinese) (刘 露,刘宛予,楚春雨,等.CT图像中肿大淋巴结肺癌转移分类方法.电子与信息学报, 2009, 31(10): 2476-2482) [23] Meier T B, Desphande A S, Vergun S, et al. Support Vector Machine Classification and Characterization of Age-Related Reorganization of Functional Brain Networks. NeuroImage. 2012, 60(1): 601-613 [24] Lin G W. The Research and Evaluation of Diagnostic Test(2). Journal of Diagnostics, 2003, 2(2): U005-U010 (in Chinese) (林果为.诊断试验的研究与评价(2).诊断学理论与实践, 2003, 2(2): U005-U010) [25] Hu M Z, Li K. A Comparative Method of the ROC Curves of Two Clinical Diagnosis. Journal of Mathematical Medicine, 2005, 18(4): 293-296 (in Chinese) (胡明珠,李 康.两种临床诊断方法效果的 ROC曲线比较.数理医药学杂志, 2005, 18(4): 293-296) [26] Song H L, He J, Yu H T, et al. Area under ROC Curves in Eva-luation and Comparison of Two Correlated Diagnostic Tests. Academic Journal of Second Military Medical University, 2006, 27(5): 562-563 (in Chinese) (宋花玲,贺 佳,虞慧婷,等.应用 ROC 曲线下面积对两相关诊断试验进行评价和比较.第二军医大学学报, 2006, 27(5): 562-563)
2015, Vol. 28 
