Triple-Modality Breast Cancer Diagnosis and Analysis in Middle Aged Women by Logistic Regression
Manjula Devarakonda Venkata1, Sumalatha Lingamgunta2
1Manjula Devarakonda Venkata, Department of CSE, Amalapuram Institute of Management Sciences & College of Engineering, Mummidivaram (A.P), India.
2Sumalatha Lingamgunta, Department of CSE, University College of Engineering, Kakinada (A.P), India.
Manuscript received on 05 February 2019 | Revised Manuscript received on 13 February 2019 | Manuscript published on 28 February 2019 | PP: 555-562 | Volume-8 Issue-4, February 2019 | Retrieval Number: D2883028419/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Breast cancer is found to be the foremost root cause of deaths associated with cancer in Asian women, and in recent days, it has become common among women out running cervical cancer. This work intends to analyse, evaluate and compare the effectiveness of the existing breast cancer imaging schemes like Ultrasound, Mammography, and Magnetic resonance imaging techniques using Logistic regression, a statistical prediction machine learning tool for diagnosing breast cancer. Using the logistic regression tool, breast cancer factor values are obtained and tabulated to compare the suggested methods. The tabulated results validate that, MRI exhibits remarkably higher sensitivity values compared to other imaging techniques such as mammography and ultrasound imaging could be ineffective in patients with cancer history and fails to diagnose some mass in dense breast tissue.
Keyword: Breast Cancer, Mammogram, Magnetic Resonance Imaging, Ultrasound, Logistic Regression.
Scope of the Article: Regression and Prediction