教師資料查詢 | 類別: 期刊論文 | 教師: 陳昆皇 KUN-HUANG CHEN (瀏覽個人網頁)

標題:Diagnosis of brain metastases from lung cancer using a modified electromagnetism like mechanism algorithm
學年103
學期1
出版(發表)日期2015/01/01
作品名稱Diagnosis of brain metastases from lung cancer using a modified electromagnetism like mechanism algorithm
作品名稱(其他語言)
著者K-H Chen; K-J Wang; A-M Adrian; K-M Wang
單位
出版者
著錄名稱、卷期、頁數Journal of Medical Systems 40(35)
摘要Brain metastases are commonly found in patients that are diagnosed with primary malignancy on their lung. Lung cancer patients with brain metastasis 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 patients’ 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 cancers microarray datasets as preliminary experiment. In addition, this method is tested on 4 lung cancer microarray public dataset. Further, we tested our method on a nationwide dataset of brain metastasis from lung cancer (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 cancer microarray datasets, a non-parametric statistical test confirmed that MEM-SVM outperformed the other methods. For the 4 lung cancer 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 brain metastasis from lung cancer 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 brain metastasis from lung cancer.
關鍵字Brain metastases;Electromagnetism like mechanism;Feature selection;Lung cancer;Support vector machine;Synthetic minority over-sampling technique
語言英文(美國)
ISSN0148-5598;1573-689X
期刊性質國外
收錄於SCI;
產學合作
通訊作者K-M Wang
審稿制度
國別美國
公開徵稿
出版型式,電子版,紙本
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