An improved machine learning approach for diagnosis of pancreatic cancer using multi modal clinical data

Abstract

Clinical diagnosis is a challenging task since data sources are newlineheterogeneous. Owing to the advancements in the healthcare field, the newlinebenefits of diagnosis have proved to exhibit an improvement. Among clinical newlineexperts, there is a positivity and hope that the use of a computer-aided newlinediagnosis system, will aid radiologists in interpreting images and better newlinedecision making for diagnosis. Human beings have always been susceptible to newlinemany kinds of life-threatening diseases. Several diseases pose a serious threat newlineto humans out of which, Pancreatic Adenocarcinoma (PDAC) is common, newlineaffecting people over 45 years of age. newlinePDAC ranks 4th in the world among cancers. Generally, the newlineconventional approach of examining individuals to diagnose the disease are newlineCT, MRI, PET scan, and Ultrasound (US). This provides a pathway for newlinebuilding an efficient pancreatic cancer diagnosis system from multi-modal newline(Structured EHRs, Jaundiced Eye images, and pancreatic CTs) clinical data to newlinepredict the risk level of individuals and detect the signs of pancreatic disease newlinefor immediate surgical planning. newlineExtracting patterns from newline