Evaluation of the efficiency of biofield diagnostic system in breast cancer detection using clinical study results and classifiers
The division of breast cancer cells results in regions of electrical depolarisation within the breast. These regions extend to the skin surface from where diagnostic information can be obtained through measurements of the skin surface electropotentials using sensors. This technique is used by the Bi...
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sg-ntu-dr.10356-992212020-03-07T13:19:23Z Evaluation of the efficiency of biofield diagnostic system in breast cancer detection using clinical study results and classifiers Subbhuraam, Vinitha Sree Ng, Eddie Yin-Kwee Kaw, G. Acharya, U. Rajendra Chong, B. K. School of Mechanical and Aerospace Engineering DRNTU::Engineering::Mechanical engineering The division of breast cancer cells results in regions of electrical depolarisation within the breast. These regions extend to the skin surface from where diagnostic information can be obtained through measurements of the skin surface electropotentials using sensors. This technique is used by the Biofield Diagnostic System (BDS) to detect the presence of malignancy. This paper evaluates the efficiency of BDS in breast cancer detection and also evaluates the use of classifiers for improving the accuracy of BDS. 182 women scheduled for either mammography or ultrasound or both tests participated in the BDS clinical study conducted at Tan Tock Seng hospital, Singapore. Using the BDS index obtained from the BDS examination and the level of suspicion score obtained from mammography/ultrasound results, the final BDS result was deciphered. BDS demonstrated high values for sensitivity (96.23%), specificity (93.80%), and accuracy (94.51%). Also, we have studied the performance of five supervised learning based classifiers (back propagation network, probabilistic neural network, linear discriminant analysis, support vector machines, and a fuzzy classifier), by feeding selected features from the collected dataset. The clinical study results show that BDS can help physicians to differentiate benign and malignant breast lesions, and thereby, aid in making better biopsy recommendations. 2013-09-24T04:24:56Z 2019-12-06T20:04:48Z 2013-09-24T04:24:56Z 2019-12-06T20:04:48Z 2010 2010 Journal Article Subbhuraam, V. S., Ng, E. Y. K., Kaw, G., Acharya, U. R., & Chong, B. K. (2010). Evaluation of the efficiency of biofield diagnostic system in breast cancer detection using clinical study results and classifiers. Journal of medical systems, 36(1), 15-24. https://hdl.handle.net/10356/99221 http://hdl.handle.net/10220/13626 10.1007/s10916-010-9441-z en Journal of medical systems |
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DRNTU::Engineering::Mechanical engineering Subbhuraam, Vinitha Sree Ng, Eddie Yin-Kwee Kaw, G. Acharya, U. Rajendra Chong, B. K. Evaluation of the efficiency of biofield diagnostic system in breast cancer detection using clinical study results and classifiers |
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The division of breast cancer cells results in regions of electrical depolarisation within the breast. These regions extend to the skin surface from where diagnostic information can be obtained through measurements of the skin surface electropotentials using sensors. This technique is used by the Biofield Diagnostic System (BDS) to detect the presence of malignancy. This paper evaluates the efficiency of BDS in breast cancer detection and also evaluates the use of classifiers for improving the accuracy of BDS. 182 women scheduled for either mammography or ultrasound or both tests participated in the BDS clinical study conducted at Tan Tock Seng hospital, Singapore. Using the BDS index obtained from the BDS examination and the level of suspicion score obtained from mammography/ultrasound results, the final BDS result was deciphered. BDS demonstrated high values for sensitivity (96.23%), specificity (93.80%), and accuracy (94.51%). Also, we have studied the performance of five supervised learning based classifiers (back propagation network, probabilistic neural network, linear discriminant analysis, support vector machines, and a fuzzy classifier), by feeding selected features from the collected dataset. The clinical study results show that BDS can help physicians to differentiate benign and malignant breast lesions, and thereby, aid in making better biopsy recommendations. |
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School of Mechanical and Aerospace Engineering |
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School of Mechanical and Aerospace Engineering Subbhuraam, Vinitha Sree Ng, Eddie Yin-Kwee Kaw, G. Acharya, U. Rajendra Chong, B. K. |
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Article |
author |
Subbhuraam, Vinitha Sree Ng, Eddie Yin-Kwee Kaw, G. Acharya, U. Rajendra Chong, B. K. |
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Subbhuraam, Vinitha Sree |
title |
Evaluation of the efficiency of biofield diagnostic system in breast cancer detection using clinical study results and classifiers |
title_short |
Evaluation of the efficiency of biofield diagnostic system in breast cancer detection using clinical study results and classifiers |
title_full |
Evaluation of the efficiency of biofield diagnostic system in breast cancer detection using clinical study results and classifiers |
title_fullStr |
Evaluation of the efficiency of biofield diagnostic system in breast cancer detection using clinical study results and classifiers |
title_full_unstemmed |
Evaluation of the efficiency of biofield diagnostic system in breast cancer detection using clinical study results and classifiers |
title_sort |
evaluation of the efficiency of biofield diagnostic system in breast cancer detection using clinical study results and classifiers |
publishDate |
2013 |
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https://hdl.handle.net/10356/99221 http://hdl.handle.net/10220/13626 |
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1681034021094555648 |