Diagnosis model of pancreatic cancer based on fusion of distribution estimation algorithm and genetic algorithm

Since the beginning of the twenty-first century, people’s living standards have been continuously improved, followed by changes in diet structure and living habits. These changes have affected the body’s endocrine system, causing lesions in the pancreatic tissue. Among these pancreatic tissue diseases, pancreatic cancer is the most harmful to human health because of its inability to find and high mortality within 1 year. At present, in the diagnosis of pancreatic cancer, medical imaging and pathological puncture are the main methods of diagnosis. These methods have a high diagnostic rate for patients with advanced pancreatic cancer, but it is difficult to apply to the diagnosis of early pancreatic cancer. In response to these problems, this paper proposes a pancreatic cancer diagnosis model based on the fusion of distribution estimation algorithm and genetic algorithm. By collecting pathological data of patients with pancreatic cancer from a hospital oncology, pathological data include clinical manifestations of pancreatic cancer patients, serum tumor markers, etc., after data preprocessing, input models, and then use different machine learning classification algorithms to make pancreatic cancer for diagnosis. By evaluating the diagnosis results of each classification algorithm, an optimal classification algorithm is obtained and applied to the diagnosis model of pancreatic cancer. The results show that compared with other classification algorithms, the model using classification algorithm has the highest accuracy, recall rate and harmonic mean, and the diagnostic performance is the best. The results show that the diagnostic model constructed in this paper has a very high application value in the early auxiliary pre-diagnosis of pancreatic cancer.