Cervical Cancer Detection: Logistic Regression vs. Neural Network

Malliboina Dinesh, S. Raj

2023

Abstract

The research aims to investigate and diagnose cervical cancer conditions by comparing the Logistic Regression algorithm with the Artificial Neural Network approach. Materials and Methods: The dataset for HPV is sourced from Kaggle, utilized for the screening and treatment of cervical cancer, and split into two subsets. Subset 1 employs the Logistic Regression algorithm, while Subset 2 employs the Artificial Neural Network method. Results: The Independent Sample T-test, applied to diagnose cervical cancer, yielded a significance level of p = 0.23 (which exceeds the widely accepted threshold of 0.05). When evaluating performance, the logistic regression approach displayed higher accuracy at 83.066% in contrast to the artificial neural network, which achieved an accuracy of 80.200%. Significantly, the superiority of the Logistic Regression technique over the Artificial Neural Network method is apparent. Conclusion: Within the realm of performance analysis, the Logistic Regression (LR) Algorithm outperforms the Artificial Neural Network (ANN) Algorithm.

Paper Citation

in Harvard Style

Dinesh M. and Raj S. (2023). Cervical Cancer Detection: Logistic Regression vs. Neural Network. In Proceedings of the 1st International Conference on Artificial Intelligence for Internet of Things: Accelerating Innovation in Industry and Consumer Electronics - Volume 1: AI4IoT; ISBN 978-989-758-661-3, SciTePress, pages 25-30. DOI: 10.5220/0012537400003739

in Bibtex Style

@conference{ai4iot23,
author={Malliboina Dinesh and S. Raj},
title={Cervical Cancer Detection: Logistic Regression vs. Neural Network},
booktitle={Proceedings of the 1st International Conference on Artificial Intelligence for Internet of Things: Accelerating Innovation in Industry and Consumer Electronics - Volume 1: AI4IoT},
year={2023},
pages={25-30},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012537400003739},
isbn={978-989-758-661-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Artificial Intelligence for Internet of Things: Accelerating Innovation in Industry and Consumer Electronics - Volume 1: AI4IoT
TI - Cervical Cancer Detection: Logistic Regression vs. Neural Network
SN - 978-989-758-661-3
AU - Dinesh M.
AU - Raj S.
PY - 2023
SP - 25
EP - 30
DO - 10.5220/0012537400003739
PB - SciTePress