Pancreatic Cancer Detection Through Hyperparameter Tuning and Ensemble Methods

Computing techniques have brought about a significant transformation in the field of medical research. Machine learning techniques have facilitated the analysis of vast amounts of data, modeling of complex scenarios, and the ability to make well-informed decisions. This presents an opportunity to develop reliable and effective medical system implementations, which may include the automatic recognition of uncertain issues related to health. Currently, significant research efforts to be directed towards the prediction of cancer, particularly focusing on addressing the various health complications caused by this disease, which can adversely impact multiple organs within the body. Pancreatic Cancer (PC) stands out as a highly lethal form of tumor, with a rather discouraging global five-year survival rate of approximately 5%. The truth behind the early detection increases the survival rate and it also helps the radiologists to give better treatment to those who are affected at early stages. Creatinine, LYVE1, REG1B, and TFF1 are urine proteomic biomarkers that offer a promising non-invasive and affordable diagnostic technique for detecting pancreatic cancer. In this study, a novel model that combines gridsearchCV technique to search and find the optimal combination of hyperparameters for a random forest classifier. In this research a new ensemble method to enhance the performance for classification of pancreatic cancer and non-cancer by using urinary biomarkers which is collected from Kaggle. The implemented model achieved better results of Accuracy 99.98%, F-1 score 99.98, Precision 99.98, and Recall 99.98.

@article{Dodda2023,
  title     = {Pancreatic Cancer Detection Through Hyperparameter Tuning and Ensemble Methods},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {12},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Koteswaramma Dodda and G. Muneeswari},
  doi       = {10.14569/IJACSA.2023.0141255},
  url       = {https://doi.org/10.14569/IJACSA.2023.0141255}
}