# Machine Learning to Tailor Intermittent Fasting for Blood Pressure Improvement

## TL;DR

A machine learning model was developed to predict which intermittent fasting protocols are most effective for lowering blood pressure in premenopausal women.

## Contribution

The study introduces a personalized machine learning framework to predict blood pressure improvement from different intermittent fasting protocols.

## Key findings

- The model achieved 77% accuracy in predicting blood pressure improvement from intermittent fasting protocols.
- IECR + FF and IECR protocols showed the highest effectiveness in improving blood pressure.
- Age and waist-to-hip ratio were critical factors influencing the success of the interventions.

## Abstract

Background/Objectives: Intermittent fasting (IF) has shown feature effectiveness in reducing blood pressure, highlighting the need for personalized intervention strategies. Methods: To address this, a machine learning framework was developed to predict the likelihood of blood pressure improvement (≥5 mmHg systolic reduction) across different IF and calorie restriction protocols in premenopausal women without diagnosed hypertension. Results: The model achieved 77% accuracy and an AUC of 0.8 in distinguishing responders from non-responders. Logistic regression analysis identified dietary intervention type as the strongest predictor of success, with Intermittent Energy and Carbohydrate Restriction + free Protein and Fat (IECR + FF) and Intermittent Energy and Carbohydrate Restriction + free Protein and Fat (IECR) protocols showing the highest effectiveness (coefficients 0.55 and 0.41 respectively). Decision tree analysis revealed age in years as a critical stratification factor, with younger patients (≤47 years) responding optimally to IECR + FF combinations, while older patients benefited from IECR, Continuous Energy Restriction (CER), or Intermittent Energy Restriction (IER) approaches. Notably, waist-to-hip ratio emerged as the strongest negative predictor, indicating that central adiposity significantly impedes blood pressure improvement regardless of intervention type. Higher baseline HDL positively predicted success, while elevated LDL and the DER diet were associated with poor outcomes. The complementary analytical approaches demonstrated that logistic regression and decision tree methods highlight different aspects of the data, with the former identifying independent linear associations and the latter suggesting potential non-linear interactions and candidate thresholds involving age years, dietary intervention type, baseline blood pressure, and metabolic markers. Conclusions: This exploratory, hypothesis-generating analysis was conducted in a cohort of premenopausal women without diagnosed hypertension and is not intended to inform clinical decision-making. The observed patterns should be interpreted as preliminary and may reflect sample-specific effects or model instability. Confirmation in larger, independent, and more diverse populations is essential before any clinical relevance can be inferred.