Application of Random Forest Model in Cancer Risk Prognosis: A Role for Dietary Habits

Abstract

Abstract Purpose- This study focuses on the impact of lifestyle behaviors, particularly eating habits, on cancer development. In recent years, there has been growing interest in predicting cancer risk using machine learning algorithms and analyzing factors such as diet. Methods- Our research utilizes a Random Forest Model to classify and identify hidden risk factors in a sample of 252 individuals from the Semnan province in Iran, split into case and control groups. Results- Seventeen dietary indicators were derived from nutritional questionnaires and used to diagnose cancer. The optimal number of trees in the model was 93, resulting in a model with high accuracy and predictive capability. The developed model achieved an average accuracy rate of 92% through cross-validation. Notable early predictors of cancer were discovered, including the extent of vegetable frying, methods of vegetable storage, and the types of containers used for daily staples like bread in Iranian cooking practices. Conclusion- Based on these findings, it is important to address and implement targeted lifestyle interventions based on early predictors to improve patient well-being and treatment outcomes. This research highlights the potential gap in current recommendations regarding health and diet for cancer patients and emphasizes the need for tailored interventions.