Survival prediction of lung cancer patients by integration of clinical and molecular features using machine learning

Syed Abdullah Basit, Rizwan Qureshi, Ali Raza Shahid, Sheheryar Khan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Among all kinds of cancer, lung cancer has the greatest fatality rate. The mutation in Epidermal growth factor receptor (EGFR) is a significant cause of cancer deaths. Lung cancer is often diagnosed at advanced cancer stages. In this work, we propose a model by integrating patient's personal information and molecular features using machine learning classifiers, and molecular dynamics simulation. The clinical information is taken from various published studies, and molecular features are extracted using the drug-protein interactions and binding free energy of the drug-protein complex. The proposed model achieves good accuracy with a random forest classifier and a deep neural network. We believe that the prediction can be a promising index, and may help physicians and oncologists to develop personalized therapies for lung cancer patients.

Original languageEnglish
Title of host publication2021 15th International Conference on Open Source Systems and Technologies, ICOSST 2021 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665413053
DOIs
Publication statusPublished - 2021
Event15th International Conference on Open Source Systems and Technologies, ICOSST 2021 - Lahore, Pakistan
Duration: 15 Dec 202116 Dec 2021

Publication series

Name2021 15th International Conference on Open Source Systems and Technologies, ICOSST 2021 - Proceedings

Conference

Conference15th International Conference on Open Source Systems and Technologies, ICOSST 2021
Country/TerritoryPakistan
CityLahore
Period15/12/2116/12/21

Keywords

  • Cancer
  • Machine learning
  • Precision medicine
  • Survival prediction

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