A Machine Learning Framework for Personalized Lifestyle Recommendations in Colorectal Cancer Prevention

Colorectal cancer (CRC) is a largely preventable disease influenced by modifiable behavioral risk factors such as diet, smoking, alcohol consumption, physical inactivity, and chronic stress. This study proposes a machine learning-based framework that generates personalized lifestyle recommendations aimed at CRC prevention. The system consists of two components: the Behavioral Recommendation Mapping Engine, which maps behavioral questionnaire responses to expertvalidated recommendations, and the Risk Assessment Module, which classifies participants into specific recommendations using supervised learning models. Eight domain-specific classifiers were developed, each targeting a key behavioral risk factor. Random Forests consistently outperformed Decision Trees, achieving high macro-averaged F1 scores even in imbalanced categories such as smoking <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(F 1=0.88)$</tex> and stress <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(F 1=0.80)$</tex>. The system also identifies the most influential behavioral variable per domain to highlight actionable risk factors. This framework will be integrated into the DIOPTRA mobile application to support realtime, personalized prevention.