Research Article Open Access

Development of Big Data Classifier for Biomedicine Early Diagnosis: An Experimental Approach Using Machine Learning Methods

Ma Beth Solas Concepcion1, Bobby Dioquino Gerardo2, Frank Ibañez Elijorde3, Joel Traifalgar De Castro4 and Nerilou Bermudez Dela Cruz5
  • 1 Department of Information Systems, Faculty of College of Information and Communications Technology, West Visayas State University, La Paz, Iloilo City, Philippines
  • 2 Division of Computer Science, Faculty of College of Information and Communications Technology, West Visayas State University, La Paz, Iloilo City, Philippines
  • 3 Division of Information Technology, Faculty of College of Information and Communications Technology, West Visayas State University, La Paz, Iloilo City, Philippines
  • 4 Division of Entertainment, Multimedia and Computing, Faculty of College of Information and Communications Technology, West Visayas State University, La Paz, Iloilo City, Philippines
  • 5 Division of Computer Science, Faculty of College of Information and Communications Technology, West Visayas State University, La Paz, Iloilo City, Philippines

Abstract

In the fast-phase world, data availability is abundant due to a rapid adaptation increase of big data technologies. Large amounts of data have been generated and collected at an unprecedented speed and scale, introducing a revolution in medical research practices for biomedicine informatics. Thus, there is an immense demand for statistically rigorous approaches, especially in the medical diagnosis discipline. Therefore, this study utilized the Bayesian Belief Network (BBN) for feature selection, which identifies relevant features from a larger set of attributes and employs a stratification for the Stochastic Gradient Descent (SGD) classifier in the classifying of breast cancer on the publicly available machine learning repository at the University of California, Irvine (UCI) such, breast cancer Wisconsin and Coimbra breast cancer datasets. The experimental approach of using BBN as feature selection achieved 0.95% coincidence. Thus, a stratified Stochastic Gradient Descent (SGD) was employed to build a classification model to validate the coincidence. Our proposed modeling classifier approach reached novelty 98%, which improved by 7% compared to the previous works. Furthermore, this study presents a web-based application, a prototype type, to employ the proposed classifier model for breast cancer diagnosis. This study expects to provide a source of confidence and satisfaction for medical physicians to use decision-support tools.

Journal of Computer Science
Volume 20 No. 4, 2024, 379-388

DOI: https://doi.org/10.3844/jcssp.2024.379.388

Submitted On: 9 September 2023 Published On: 1 February 2024

How to Cite: Concepcion, M. B. S., Gerardo, B. D., Elijorde, F. I., De Castro, J. T. & Dela Cruz, N. B. (2024). Development of Big Data Classifier for Biomedicine Early Diagnosis: An Experimental Approach Using Machine Learning Methods. Journal of Computer Science, 20(4), 379-388. https://doi.org/10.3844/jcssp.2024.379.388

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Keywords

  • Biomedicine Diagnosis Application
  • Big Data
  • Data Mining
  • Classification Algorithm
  • Bayesian Belief Network