Cancer XAI: A Responsible Model for Explaining Cancer Drug Prediction Models

Authors

  • Sonali Kothari Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, India
  • Rutuja Rajendra Patil Computer Science and Engineering-Artificial Intelligence & Machine Learning Department, Vishwakarma Institute of Information Technology, Pune
  • Shivanandana Sharma Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, India
  • Aqsa Kazi Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, India
  • Michela D'Silva Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, India
  • Sanskruti Shejwal Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, India
  • M. Karthikeyan NCL-CSIR, Baner, Pune

Keywords:

Artificial Intelligence, Explainable AI, Ensemble Model, Random Forest Classifier, XAI

Abstract

There has been a growing interest in using Explainable Artificial Intelligence (XAI) for healthcare in recent years. An explainable artificial intelligence (XAI) model for cancer diagnosis is suggested in this research paper. The model offers data that can be understood and explained, essential for medical decision-making. It also makes reliable forecasts. Compared to other models, the proposed model performs at the cutting edge thanks to training and evaluation on a sizable dataset of cancer images. The significance of interpretability in medical applications is also covered in the paper, along with how the suggested model resolves this issue. The findings of this study show how XAI models have the potential to increase cancer detection and provide more transparent and reliable medical decision-making.

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Published

21.09.2023

How to Cite

Kothari, S. ., Patil, R. R. ., Sharma, S. ., Kazi, A. ., D’Silva, M. ., Shejwal, S. ., & Karthikeyan, M. . (2023). Cancer XAI: A Responsible Model for Explaining Cancer Drug Prediction Models. International Journal of Intelligent Systems and Applications in Engineering, 11(4), 472–484. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3582

Issue

Vol. 11 No. 4 (2023)

Section

Research Article

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