Prediction of Pancreatic Cancer Based on Data and Artificial Intelligence

Abstract

This research explores the importance of implementing intelligent systems based on artificial intelligence and data science for the prediction and diagnosis of pancreatic cancer, addressing a critical problem related to the high mortality rate and the difficulty of early diagnosis of this disease. It underscores its crucial role in improving survival rates and is aligned with Sustainable Development Goal 3 (SDG 3), which seeks to ensure healthy lives and promote well-being for all, as well as with SDG 9, which promotes the construction of resilient infrastructure and innovation. The overall objective of this research was to identify the best technological tools for preventive diagnosis of pancreatic cancer based on artificial intelligence and data science. This research was basic, with a qualitative approach and descriptive design, using databases such as Scopus and ScienceDirect to collect relevant information and performing documentary analysis of secondary sources. The main results reveal significant challenges, such as the selection of optimal cutoffs to balance sensitivity and specificity, the integration of clinical and genomic data, and the need for explainable models that can handle multimodal data. Notable benefits include early detection of pancreatic cancer, reduced workload for healthcare professionals, and improved diagnostic accuracy. Success stories demonstrating high levels of accuracy in pancreatic cancer classification using advanced techniques such as convolutional neural networks and deep learning are highlighted. In conclusion, the implementation of intelligent systems based on artificial intelligence is essential to improve the detection and treatment of pancreatic cancer, as they improve diagnostic accuracy and efficiency and also contribute to the creation of technological innovations with great social impact.

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