The study that applies artificial intelligence and logistic regression for assistance in differential diagnostic of pancreatic cancer

摘要

Recent medical researches indicate that pancreatic cancer is the eighth leading cause of death of the populations in Taiwan. Each year approximately 800 victims die of this cancer, and the number is increasing year by year. Since most early symptoms of pancreatic cancer is non-specific, doctors' diagnostic decisions might differ based on individual experience, knowledge of the disease, and influenced by their mental conditions at examinations. Certain diagnostic errors are inevitable to occur and mislead the following treatment plans and thus result in insignificant and inefficient follow-up tests. This phenomenon not only caused wastes of the medical resources but also severely delay the golden timing of "early detection and early treatment" for patients. This study used artificial neural network and genetic algorithm of artificial intelligence (Al) as well as logistic regression of statistics to construct three types of screening models for pancreatic cancer and acute pancreatitis. Additionally, it adopted the ROC curves to compare and analyze discriminations of the above-mentioned three screening models. It used 234 case patient data as its training samples and 117 cases as test data. The results of pairwise comparisons and analysis indicate that AUC values of the tree models have no significant differences regardless. Except the fact that CALR model is obviously better than SLR model, both the pairwise comparisons between SLR and BPN or BPN and GALR have no significant difference. On the contrary, however, if under the condition of obtaining the optimal threshold of the three models, CALR model has the best performance with 96.7% in sensitivity and 82.5% in specificity, which are both better than SLR model with 96.7% in sensitivity and 73.7% in specificity and BPN model with 88.3% in sensitivity and 84.2% in specificity. Finally, artificial intelligent approaches will have more optimal predictions in the future with larger and more comprehensive data base as well as more accurate computing methods.