Comparative Analysis of Data Mining Algorithms for Cancer Gene Expression Data

Cancer is amongst the most challenging disorders to diagnose nowadays, and experts are still struggling to detect it on early stage. Gene selection is significant for identifying cancer-causing different parameters. The two deadliest cancers namely, colorectal cancer and breast malignant, is found in male and female, respectively. This study aims at predicting the cancer at an early stage with the help of cancer bioinformatics. According to the complexity of illness metabolic rates, signaling, and interaction, cancer bioinformatics is among strategies to focus bioinformatics technologies like data mining in cancer detection. The goal of the proposed study is to make a comparison between support vector machine, random forest, decision tree, artificial neural network, and logistic regression for the prediction of cancer malignant gene expression data. For analyzing data against algorithms, WEKA is used. The findings show that smart computational data mining techniques could be used to detect cancer recurrence in patients. Finally, the strategies that yielded the best results were identified.

@article{Thareja2021,
  title     = {Comparative Analysis of Data Mining Algorithms for Cancer Gene Expression Data},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {12},
  number    = {10},
  year      = {2021},
  publisher = {The Science and Information Organization},
  author    = {Preeti Thareja and Rajender Singh Chhillar},
  doi       = {10.14569/IJACSA.2021.0121035},
  url       = {https://doi.org/10.14569/IJACSA.2021.0121035}
}