Integrating knowledge representation/engineering, the multivariant PNN and machine learning to improve breast cancer diagnosis

Walker H. Land Jr.; Mark J. Embrechts; Frances R. Anderson; Tom Smith; Lut Wong; Steve Fahlbusch; Robert Choma

doi:10.1117/12.604575

28 March 2005 Integrating knowledge representation/engineering, the multivariant PNN, and machine learning to improve breast cancer diagnosis

Walker H. Land Jr., Mark J. Embrechts, Frances R. Anderson, Tom Smith, Lut Wong, Steve Fahlbusch, Robert Choma

Author Affiliations +

Proceedings Volume 5812, Data Mining, Intrusion Detection, Information Assurance, and Data Networks Security 2005; (2005) https://doi.org/10.1117/12.604575
Event: Defense and Security, 2005, Orlando, Florida, United States

Abstract

Breast cancer is second only to lung cancer as a tumor-related cause of death in women. Currently, the method of choice for the early detection of breast cancer is mammography. While sensitive to the detection of breast cancer, its positive predictive value (PPV) is low. One of the main deterrents to achieving high computer aided diagnostic (CAD) accuracy is carelessly developed databases. These “noisy” data sets have always appeared to disrupt learning agents from learning correctly. A new statistical method for cleaning data sets was developed that improves the performance of CAD systems. Initial research efforts showed the following: PLS Az value improved by 8.79% and partial Az improved by 49.71%. The K-PLS Az value at Sigma 4.1 improved by 9.18% and the partial Az by 43.47%. The K-PLS at Sigma 3.6 (best fit sigma with this data set) Az value improved by 9.24% and the partial Az by 44.29%. With larger data sets, the ROC curves potentially could look much better than they do now. The Az value for K-PLS (0.892565) is better than PLS, PNN, and most SVMs. The SVM-rbf kernel was the only agent that out performed the K-PLS with an Az value of 0.895362. However, K-PLS runs much faster and appears to be just as accurate as the SVM-rbf kernel.

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Walker H. Land Jr., Mark J. Embrechts, Frances R. Anderson, Tom Smith, Lut Wong, Steve Fahlbusch, and Robert Choma "Integrating knowledge representation/engineering, the multivariant PNN, and machine learning to improve breast cancer diagnosis", Proc. SPIE 5812, Data Mining, Intrusion Detection, Information Assurance, and Data Networks Security 2005, (28 March 2005); https://doi.org/10.1117/12.604575

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KEYWORDS

Breast cancer

Data modeling

Principal component analysis

Neural networks

Computer aided diagnosis and therapy

Databases

Machine learning

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