NDLI: Diagnosis of Brain Metastases from Lung Cancer Using a Modified Electromagnetism like Mechanism Algorithm

Content Provider	Springer Nature Link
Author	Chen, Kun Huang Wang, Kung Jeng Adrian, Angelia Melani Wang, Kung Min Teng, Nai Chia
Copyright Year	2015
Abstract	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.
Starting Page	1
Ending Page	14
Page Count	14
File Format	PDF
ISSN	01485598
Journal	Journal of Medical Systems
Volume Number	40
Issue Number	1
e-ISSN	1573689X
Language	English
Publisher	Springer US
Publisher Date	2015-11-14
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Brain metastases Electromagnetism like mechanism Feature selection Lung cancer Support vector machine Synthetic minority over-sampling technique Health Informatics Statistics for Life Sciences, Medicine, Health Sciences
Content Type	Text
Resource Type	Article
Subject	Health Information Management Information Systems Health Informatics

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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