Investigation of MIHP Codes Over Free Space Optical Communication

  • Mohamed Ismail A M et al.
Keywords: Breast cancer, Voting classifier, Machine learning models, Classification performance.

Abstract

Introduction: Breast cancer has become the greatest frequent cancer among worldwide. Machine learning techniques contribute much to cancer prognosis.

Objectives: The prime focus of the work is to enhance the prognosis of breast cancer at an earlier stage using an ensemble of machine learning classifiers.

Methods: Next generation genetic sequences of homo sapiens, BRCA1 and BRCA2 from National Centre for Biotechnology Information were derived for prediction of breast cancer. The proposed ensembled classifiers by hard voting and soft voting, combined models like Decision Tree technique, SVM algorithm, LR statistical model, Linear Discriminant analysis model, Naive Bayes classifier and k-nearest neighbours’ algorithm.

Results: Five ensembled models from 6 machine learning classifiers were concatenated for the prediction purpose. Classification accuracy of ensemble hard voting and soft voting classifiers were evaluated statistically. Soft voting classifier for model 1(DT & SVM) and model 2(DT, SVM &LR) achieved greatest value for classification performance metrics.

Conclusion: Among all ensembled models, model 1 as well as model 2 achieved maximum classification precision of 94%.

Author Biography

Mohamed Ismail A M et al.

Mohamed Ismail A M1, Sadham Hussain K2, Sheik Alaudin A3
1,2Assistant Professor ,3Final Year M.E. VLSI Design
Department of Electronics and Communication Engineering, Al-Ameen Engineering College (Autonomous), Erode – 638 104, Tamilnadu, India

Published
2024-02-04
Issue
Volume 2024
Section
Regular Issue

Letters in High Energy Physics (LHEP) is an open access journal. The articles in LHEP are distributed according to the terms of the creative commons license CC-BY 4.0. Under the terms of this license, copyright is retained by the author while use, distribution and reproduction in any medium are permitted provided proper credit is given to original authors and sources.

Terms of Submission

By submitting an article for publication in LHEP, the submitting author asserts that:

1. The article presents original contributions by the author(s) which have not been published previously in a peer-reviewed medium and are not subject to copyright protection.

2. The co-authors of the article, if any, as well as any institution whose approval is required, agree to the publication of the article in LHEP.