PAD: A Pancreatic Cancer Detection based on Extracted Medical Data through Ensemble Methods in Machine Learning

The considerable research into medical health systems is allowing computing systems to develop with the most cutting-edge innovations. These developments are paving the way for more efficient medical system implementations, including automatic identification of health-related disorders. The most important health research is being done to predict cancer, which can take several forms and affect many parts of the body. One of the most prevalent tumors that is expected to be incurable is pancreatic cancer. Pancreatic cancer is one of the most common cancers that is projected to be incurable. Previous research has found that a panel of three protein biomarkers (LYVE1, REG1A, and TFF1) found in urine can help detect respectable PDAC. To improve this panel in this study by replacing REG1A with REG1B from extracted data sets into CSV format. Finally, will analyze four significant biomarkers that are found in urine, creatinine, LYVE1, REG1B, and TFF1. Creatinine is a protein that is commonly utilized as a kidney function indicator. Lymphatic vessel endothelial hyaluronan receptor 1 (YVLE1) is a protein that may help tumors spread. REG1B is a protein that has been linked to pancreatic regeneration, while TFF1 is trefoil factor 1, which has been linked to urinary tract regeneration and repair It's impossible to treat it properly once it's been diagnosed. Machine learning and neural networks are now showing promise for accurate pancreatic picture segmentation in real time for early diagnosis. This research looks at how to analyze pancreatic tumors using ensemble approaches in machine learning. According to preliminary data, the proposed technique looks to improve the classifier's performance for early diagnosis of pancreatic cancer.