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Journal of Medical Systems

, Volume 36, Issue 1, pp 15–24 | Cite as

Evaluation of the Efficiency of Biofield Diagnostic System in Breast Cancer Detection Using Clinical Study Results and Classifiers

  • Vinitha Sree Subbhuraam
  • E. Y. K. Ng
  • G. Kaw
  • Rajendra Acharya U
  • B. K. Chong
Original Paper

Abstract

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.

Keywords

Breast cancer Electropotentials Biofield Diagnostic System Classifiers Clinical Study 

Notes

Conflict of interest statement

None declared.

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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Vinitha Sree Subbhuraam
    • 1
  • E. Y. K. Ng
    • 1
    • 2
  • G. Kaw
    • 3
  • Rajendra Acharya U
    • 4
  • B. K. Chong
    • 3
  1. 1.Advanced Design & Modelling Lab 1, School of Mechanical & Aerospace Engineering, Block N3, Level 1Nanyang Technological UniversitySingaporeSingapore
  2. 2.Adjunct NUH Scientist, Office of Biomedical ResearchNational University HospitalSingaporeSingapore
  3. 3.Consultant Radiologist, Department of Diagnostic RadiologyTan Tock Seng HospitalSingaporeSingapore
  4. 4.School of Engineering, Division of ECENgee Ann PolytechnicSingaporeSingapore

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