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Indian Journal of Pharmaceutical Education and ResearchNOW IN
Article

Exploring Machine Learning Models for Recurrence Prediction in Lung Cancer Patients

Priyanka Ramesh1, Anika Jain1,2, Ramanathan Karuppasamy1, Shanthi Veerappapillai1★★ Corresponding author

  1. 1Department of Biotechnology, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu, INDIA.
  2. 2Biological Sciences Graduate Student, Purdue University, West Lafayette, IN, US.

CORRESPONDENCE

Shanthi Veerappapillai

Department of Biotechnology, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu, INDIA.

shanthi.v@vit.ac.in

Received: 02-01-2021; Revised: 12-12-2021; Accepted: 04-06-2022.

Volume 56, Issue 3S · pp. S398–S406 · PUBLISHED 2022 · DOI: 10.5530/ijper.56.3s.147

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ABSTRACT

Background: A proper assessment for the probability of recurrence in lung cancer is mandatory for a clinician to make an effective treatment-decision. Materials and Methods: Here, we employed machine learning algorithms to predict the lung cancer recurrence rate using the Caribbean and few white ethnicities populations. A 100 metastatic record with 15 predictor variables and 1 dependent variable was considered for model development. These models were evaluated using seven performance metrics, including accuracy and F1 score. Results: Our study results show that the decision tree outperformed the other models with the highest accuracy and F1 score of about 0.95 and 0.90, respectively. Of note, the p-value and correlation matrix show that the most significant features accounting for the tumor recurrence are cancer stage, ethnicity, tumor size, genome doubled and time to recurrence. Conclusion: Thus, our study provides insights into implementing machine learning algorithms to evaluate cancer outcomes in a clinical setting.

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    Ramesh, P., Jain, A., Karuppasamy, R., & Veerappapillai, S. (2022). Exploring Machine Learning Models for Recurrence Prediction in Lung Cancer Patients. Indian Journal of Pharmaceutical Education and Research, 56(3S), S398–S406. https://doi.org/10.5530/ijper.56.3s.147