Performance Analysis of Poly Cystic Ovary Syndrome (PCOS) using Broyden’s Kernel Import Point (BKIP) Classifier

Authors

  • Rakshitha Kiran Assistant Professor, Department of MCA, Research scholar at ISE, Dayananda Sagar College of Engineering, VTU University, Bengaluru, Karnataka, India
  • Naveen NC Professor and Head, Department of Computer Science & Engineering, JSS Academy of Technical Education, VTU University, Bengaluru, Karnataka, India

Keywords:

Broyden, Import Vector Machine, Support Vector Machine, Machine Learning

Abstract

Machine Learning (ML) is a progressive and immensely approached technological application which became an enormous trend within the industry. ML is widely utilized in various applications and can be utilized by healthcare companies to acquire valuable data that can be used to diagnose diseases in the earlier stage. In this paper, a new classification approach Broyden’s Kernel Import Point (BKIP) classifier is proposed and summarizes the use and its application in healthcare domain. BKIP classifier is a new approach used for classification which is built on Kernel Logistic Regression and Import Vector Machine (IVM). Data for the current study project was gathered from the ESIC Hospital in Bengaluru, India, and it was found that the BKIP algorithm's computational cost was significantly lower than that of the Support Vector Machine (SVM) and the IVM.  The paper provides the implementation of the BKIP classification algorithm and it is noted that when applied to Polycystic Ovary Syndrome (PCOS) data, the model produced an accurate result of 89.1 %.

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Overall working of BKIP Classifier

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Published

16.12.2022

How to Cite

Kiran, R. ., & NC , N. . (2022). Performance Analysis of Poly Cystic Ovary Syndrome (PCOS) using Broyden’s Kernel Import Point (BKIP) Classifier. International Journal of Intelligent Systems and Applications in Engineering, 10(4), 307–312. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2263

Issue

Vol. 10 No. 4 (2022)

Section

Research Article

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