Modern medicine has made immense advance in the medical diagnostic techniques, which benefits the professors an increasingly growing number of physical exam projects with higher accuracy. The result is that, nowadays, it has the promise to make precise disease prevention through monitoring the indexes of the patient's biochemical indicators. In the past, determining the biochemical indicators for a disease was a crucial issue and was mainly carried out with the domain experts' experience and professional knowledge. However, this may be time-consuming and may have added some subjectivity and partial opinions. As a result, there has been growing interest in targeting the biochemical indicators fast and right with the aid of computer science. In this paper, we adopt the feature selection algorithm for Clusters (FSC) to mine a set of the chosen SGA indicators aiming to find the ones with the most characteristic strength. For the stage of feature selection, we cluster the features and find the top variable which shares the most discriminative information. Finally, to exam the efficacy of the FSC, four widely used classifiers are implemented in the experiment. The results show that for the prediction of SGA, the FSC can reach a prediction precision of 78.81%, which validates the feasibility of applying this algorithm to mining the indicators for SGA.
['Haowen Mo', 'Lu Liu', 'Jianqiang Li', 'Ji-Jiang Yang', 'Xi Meng', 'Shi Chen', 'Hui Pan']
Risk Factors Selection for SGA Prediction