A Machine Learning Approach to Select the Type of Intermittent Fasting in Order to Improve Health by Effects on Type 2 Diabetes

Shula Shazman

Abstract

Intermittent fasting (IF) is the cycling between periods of eating and fasting. The main types of IF are: complete alternate-day fasting; time-restricted feeding (eating within specific time frames such as the most prevalent 16:8 fast, with 16 hours of fasting and 8 hours for eating); religious fasting such as the Ramadan (occurs one month per year, with eating taking place only after nightfall). IF can be effective in reducing metabolic disorders and age-related diseases by bringing about changes in metabolic parameters associated with type 2 diabetes. Questions do remain, however, about the effects of the different types of IF as a function of the age at which fasting begins, gender and severity of type 2 diabetes. In this paper we describe a machine learning approach to selecting the best type of IF to improve health in type 2 diabetes. For the purposes of this research, the health outcomes of interest are changes in fasting glucose and insulin. The different types of intermittent fast offer promising non-pharmacological approaches to improving health at the population level, with multiple public health benefits.

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Paper Citation


in Harvard Style

Shazman S. (2020). A Machine Learning Approach to Select the Type of Intermittent Fasting in Order to Improve Health by Effects on Type 2 Diabetes.In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS, ISBN 978-989-758-398-8, pages 131-137. DOI: 10.5220/0008950201310137


in Bibtex Style

@conference{bioinformatics20,
author={Shula Shazman},
title={A Machine Learning Approach to Select the Type of Intermittent Fasting in Order to Improve Health by Effects on Type 2 Diabetes},
booktitle={Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS,},
year={2020},
pages={131-137},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008950201310137},
isbn={978-989-758-398-8},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS,
TI - A Machine Learning Approach to Select the Type of Intermittent Fasting in Order to Improve Health by Effects on Type 2 Diabetes
SN - 978-989-758-398-8
AU - Shazman S.
PY - 2020
SP - 131
EP - 137
DO - 10.5220/0008950201310137