Exploring Machine Learning Models for Recurrence Prediction in Lung Cancer Patients

Indian Journal of Pharmaceutical Education and Research

  • Priyanka Ramesh1Department of Biotechnology, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu, INDIA.
  • Anika Jain1Department of Biotechnology, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu, INDIA., 2Biological Sciences Graduate Student, Purdue University, West Lafayette, IN, US.
  • Ramanathan Karuppasamy1Department of Biotechnology, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu, INDIA.
  • Shanthi Veerappapillai1Department of Biotechnology, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu, INDIA.

Volume 56 Issue 3s Pages S398-S406

DOI: 10.5530/ijper.56.3s.147

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.

Keywords

  • chine learning
  • Lung cancer
  • Recurrence
  • Statistical analysis
  • Correlation
  • matrix
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