Prediction of recurrent spontaneous abortion using evolutionary machine learning with joint self-adaptive sime mould algorithm

Abstract

Recurrent spontaneous abortion (RSA) is a frequent abnormal pregnancy with long-term psychological repercussions that disrupt the peace of the whole family. In the diagnosis and treatment of RSA worsened by thyroid disorders, recurrent spontaneous abortion is also a significant obstacle. The pathogenesis and possible treatment methods for RSA are yet unclear. Using clinical information, vitamin D and thyroid function measurements from normal pregnant women with RSA, we attempt to build a framework for conducting an effective analysis for RSA in this research. The framework is presented by combining the joint self-adaptive sime mould algorithm (JASMA) with the common kernel learning support vector machine with maximum-margin hyperplane theory, abbreviated as JASMA-SVM. The JASMA has a complete set of adaptive parameter change methods, which improves the algorithm's global search and optimization capabilities and guarantees that it speeds convergence and departs from the local optimum. On CEC 2014 benchmarks, the property of JASMA is validated, and then it is utilized to concurrently optimize parameters and select optimal features for SVM on RSA data from VitD, thyroid hormone levels, and thyroid autoantibodies. The statistical results demonstrate that the proposed JASMA-SVM can be treated as a potential tool for RSA with accuracy of 92.998%, MCC of 0.92425, sensitivity of 93.286%, specificity of 93.064%.

Keywords: Feature selection; Kernel learning; Parameter optimization; Recurrent spontaneous abortion; Self-adaptive sime mould algorithm; Support vector machine.