The mother's internal and external environment during the developmental stages of the fetus influences the health of the offspring. According to the evolutionary origins of health and disease theory,
environmental factors influence offspring and also affect health in adulthood. Recently, studies based on this theory have attracted attention for their clinical utility in identifying risk groups for various diseases.
Neurodevelopmental disorders (NDD) such as autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) are on the rise globally and may be caused by exposure to certain
environments, lifestyle (including diet) prenatally during pregnancy. Researching the determinants of prenatal environment and lifestyle on the mechanism of NDD onset serves to fill a useful knowledge gap
in preventing the increase of these disorders and disseminating preventive medicine.
The goal of this research is to find out how different types of prenatal data (like lifestyle, nutrition, sociodemographics, and environment) interact to make a prediction algorithm (conversion score) for how
a child's brain develops. In this way, the statistical analysis of the dataset will try to find the most accurate predictors of how children's neurodevelopment and NDD will turn out. We'll make a machine learning
algorithm (like a support vector machine or a random forest) to rank the predictors by how well they can predict and choose a weighted average of that power.