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| Algorithm for Feature Extraction with Effective Medical Meaning for the Prenatal Diagnosis of Placenta Accreta |
| PAN Xiao-Xiao1, YE Dong-Yi1, YAN Jian-Ying2, ZHANG Dong1, YANG Dan-Lin2 |
1.College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350001
2.Maternal and Child Health Hospital of Fujian Province, Fuzhou 350001 |
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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.
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Received: 20 May 2014
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2015, Vol. 28 
