UNASSIGNED: Brain metastases are commonly found in @entity1 that are diagnosed with primary @entity5 on their lung. @entity2 @entity1 with @entity75 tend to have a poor survivability, which is less than 6  months in median. Therefore, an early and effective detection system for such disease is needed to help prolong the @entity1 ' survivability and improved their quality of life. A modified electromagnetism-like mechanism (EM) algorithm, MEM-SVM, is proposed by combining EM algorithm with support vector machine (SVM) as the classifier and opposite sign test (OST) as the local search technique. The proposed method is applied to 44 UCI and IDA datasets, and 5 @entity5 microarray datasets as preliminary experiment. In addition, this method is tested on 4 @entity2 microarray public dataset. Further, we tested our method on a nationwide dataset of @entity75 from @entity2 (BMLC) in Taiwan. Since the nature of real medical dataset to be highly imbalanced, the synthetic minority over-sampling technique (SMOTE) is utilized to handle this problem. The proposed method is compared against another 8 popular benchmark classifiers and feature selection methods. The performance evaluation is based on the accuracy and Kappa index. For the 44 UCI and IDA datasets and 5 @entity5 microarray datasets, a non-parametric statistical test confirmed that MEM-SVM outperformed the other methods. For the 4 @entity2 public microarray datasets, MEM-SVM still achieved the highest mean value for accuracy and Kappa index. Due to the imbalanced property on the real case of BMLC dataset, all methods achieve good accuracy without significance difference among the methods. However, on the balanced BMLC dataset, MEM-SVM appears to be the best method with higher accuracy and Kappa index. We successfully developed MEM-SVM to predict the occurrence of @entity75 from @entity2 with the combination of SMOTE technique to handle the class imbalance properties. The results confirmed that MEM-SVM has good diagnosis power and can be applied as an alternative diagnosis tool in with other medical tests for the early detection of @entity75 from @entity2 .

Diagnosis of XXXX from Lung Cancer Using a Modified Electromagnetism like Mechanism Algorithm.

@entity1 :: ('9606', 'Species') :: ['patients'] @entity2 :: ('MESH:D008175', 'Disease') :: ['Lung cancer', 'lung cancer'] @entity75 :: ('MESH:D001927', 'Disease') :: ['brain metastasis'] @entity5 :: ('MESH:D009369', 'Disease') :: ['cancers', 'cancer', 'malignancy']
@entity75 :: (MESH:D001927,Disease) :: ['Brain Metastases']
