Multivariate Analysis on Personalized Cancer Data using a Hybrid Classification Model using Voting Classifier

Authors

  • Ashok Reddy Kandula Research Scholar, Department of Computer Science and Engineering, Annamalai University, Annamalai Nagar, Chidambaram, Tamil Nadu-608002, India
  • R. Sathya Assistant Professor, Department of Computer Science and Engineering, Annamalai University, Annamalai Nagar, Chidambaram, Tamil Nadu-608002, India
  • S. Narayana Professor, Department of Computer Science and Engineering, Seshadri Rao Gudlavalleru Engineering College, Gudlavalleru-521356, India

Keywords:

Artificial Intelligence, Medical data analysis, gene mutation, ensemble, Logistic Regression, Support Vector Machine, Random Forest

Abstract

Cancer was found to be a significant burdening type of problem in the medical system. An accurate diagnosis is considered to be a challenging task for physicians. Modern Artificial Intelligence (AI) research proves that cancers are easily identifiable and early detectable and can be diagnosed by classification with the help of gene mutations that occur in the cells. This paper presented a hybrid classification model using an ensemble machine learning technique to classify the type of cancer class from the given medical dataset. The proposed hybrid voting classifier utilizes guided WOA (Whale Optimization Algorithm) that aggregates different classification models' prediction results to choose the most voted class. This helps to increase the chance that individual classifiers, e.g., Logistic Regression (LR), Support Vector Machines (SVM), and Random Forest (RF), show significant discrepancies. The results were found to be better compared to the existing work.

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Published

04.02.2023

How to Cite

Kandula, A. R. ., Sathya, R. ., & Narayana, S. . (2023). Multivariate Analysis on Personalized Cancer Data using a Hybrid Classification Model using Voting Classifier. International Journal of Intelligent Systems and Applications in Engineering, 11(1), 354–362. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2546

Issue

Vol. 11 No. 1 (2023)

Section

Research Article

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