Nutritional and lifestyle predictors of rectal bleeding in functional constipation: A machine learning approach

Rectal bleeding among young adults is an increasingly common clinical concern often linked with chronic constipation and unhealthy lifestyle habits. Early identification of at-risk individuals through machine learning models-based approach may help in prevention and targeted intervention.

We aim to identify dietary and lifestyle risk factors for rectal bleeding and to develop machine learning-based models for risk prediction.

A descriptive observational study was conducted on 875 Indian college going participants. A structured questionnaire assessed fiber intake, physical activity, constipation symptoms, and body mass index (BMI). Multiple machine learning algorithms were evaluated, and their performance was assessed using accuracy and area under the receiver operating characteristic curve (ROC-AUC).

Low intake of boiled vegetables or oatmeal (<50 g/day) was associated with a 43.92 % bleeding rate (p < 0.001). Participants consuming inadequate whole grains (>25 g/day) showed a 44.81 % bleeding rate. Overweight or obese individuals exhibited a significantly higher bleeding incidence (12.26 %) than those with normal BMI (5.55 %; p = 0.008). The KNeighbors Classifier showed the highest accuracy (98.86 %) and ROC-AUC (0.994). Variables related to symptoms had greater predictive importance than those related to lifestyle.

The findings support the role of dietary fiber and BMI in the development of rectal bleeding in constipated individuals. The predictive models demonstrate strong potential for identifying at-risk individuals and is considered a simple and useful tool for predicting rectal bleeding in functional constipation, suggesting preventive health strategies and dietary modifications. This novel algorithm might enable clinicians to perform personalized dietary strategies with improved clinical outcomes. Further validation across larger and more diverse populations is recommended.