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Hypertension: Constraining the Expression of ACE-II by Adopting Optimal Macronutrients Diet Predicted via Support
Mohammad Farhan Khan1, Gazal Kalyan2, Sohom Chakrabarty3
1Digby Stuart College, University of Roehampton, London SW15 5PU, UK.
Nutrients
|July 27, 2022
Summary
This study uses machine learning to predict food-derived peptides that can naturally control hypertension. This approach offers a safer alternative to ACE inhibitors for hypertensive patients, especially during the COVID-19 pandemic.
Area of Science:
- Biotechnology
- Computational Biology
- Nutritional Science
Background:
- Hypertension affects 1.13 billion people globally, with increased COVID-19 fatality risk.
- Angiotensin-converting enzyme (ACE) inhibitors, common hypertension drugs, carry metabolic risks and may increase ACE-II expression, facilitating COVID-19 infection.
- Dietary interventions, such as optimal macronutrient intake, present a potential alternative for hypertension management.
Purpose of the Study:
- To develop a machine learning model for predicting food-derived antihypertensive peptides.
- To reduce computational load while maintaining prediction accuracy using feature selection.
- To identify dietary components as a safer alternative for hypertension management, particularly for individuals at risk during the COVID-19 pandemic.
Main Methods:
- Utilized a nontrivial feature selection algorithm combined with a support vector machine (SVM).
- Employed feature filtering to identify dominant patterns in the feature space, optimizing computational efficiency.
- Evaluated model performance based on prediction accuracy and reduction of Type I error.
Main Results:
- Achieved maximum accuracies of 86.17% and 85.61% with the best-performing SVM models after feature selection.
- The proposed feature filtering algorithm demonstrated a favorable trade-off, reducing Type I errors.
- Successfully identified potential food-derived antihypertensive peptides through intelligent prediction.
Conclusions:
- Machine learning, specifically SVM with feature selection, can effectively predict food-derived antihypertensive peptides.
- This approach offers a promising, natural alternative for hypertension management, mitigating risks associated with conventional medications.
- The findings support the integration of dietary strategies into hypertension treatment plans, especially in vulnerable populations during health crises.
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