Deploying machine learning to examine data from 459,169 participants in the UK Biobank, the study identified 84 features that could signal increased cancer risk. Several markers also signalled chronic kidney or liver disease, highlighting the significance of exploring the underlying pathogenic mechanisms of these diseases for their potential connections with cancer. The study, Hypothesis-free discovery of novel cancer predictors using machine learning, was conducted by UniSA researchers: Dr Iqbal Madakkatel , Dr Amanda Lumsden , Dr Anwar Mulugeta , and Professor Elina Hyppönen , with University of Adelaide's Professor Ian Olver .

https://www.miragenews.com/ai-aids-in-identifying-cancer-risk-factors-1075195/

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