System and method for generating a gestational disorder nourishment program

1. A system for generating a gestational disorder nourishment program, the system comprising:

a computing device, the computing device configured to:

obtain a maternal marker;

calculate a gestational phase, wherein calculating the gestational phase comprises:

identifying a gestational goal; and

training a gestational machine learning model using a gestational training set, wherein the gestational training set correlates a plurality of maternal markers and a plurality of gestational goals to a plurality of gestational phases;

inputting the maternal marker and the gestational goal to the trained gestational machine learning model; and

outputting the gestational phase from the trained gestational machine learning model as a function of the maternal marker and the gestational goal;

produce a nourishment demand as a function of the gestational phase;

determine an edible, wherein determining the edible comprises:

training an edible machine-learning model using an edible training set, wherein the edible training set correlates a plurality of nutrition demands to a plurality of edibles;

inputting the nourishment demand to the trained edible machine-learning model; and

outputting the edible from the trained edible machine-learning model as a function of the nourishment demand; and

generate a nourishment program as a function of the edible, wherein each of the gestational machine-learning model and the edible machine-learning model is a machine learning process selected from the group consisting of: K-nearest neighbors, support vector machines, kernel support vector machines, naïve bayes, decision tree classification, random forest classification, K-means clustering, hierarchical clustering, dimensionality reduction, principal component analysis, linear discriminant analysis, kernel principal component analysis, Q-learning, State Action Reward State Action (SARSA), Deep-Q network, Markov decision processes and Deep Deterministic Policy Gradient (DDPG).

2. The system of claim 1 , wherein obtaining the maternal marker further comprises identifying an uncertainty indicator and obtaining the maternal marker as a function of the uncertainty indicator.

3. The system of claim 1 , wherein calculating the gestational phase further comprises:

determining that the maternal marker is not suitable for a first gestational phase; and

determining that the maternal marker is suitable for a second gestational phase wherein the second gestational phase occurs after the first gestational phase.

4. The system of claim 1 , wherein the computing device is further configured to determine a gestational divergence as a function of the gestational phase and a divergence threshold.

5. The system of claim 1 , wherein calculating the gestational phase further comprises:

receiving a conception datum;

classifying the conception datum to a gestational progression level; and

calculating the gestational phase as a function of the classifying.

6. The system of claim 1 , wherein calculating the gestational phase further comprises determining a gestational disorder and producing the gestational phase as a function of the gestational disorder.

7. The system of claim 6 , wherein determining the gestational disorder further comprises:

obtaining a disorder training set that correlates at least a gestational enumeration and a gestational effect to the gestational disorder; and

determining the gestational disorder as a function of the maternal marker using a disorder machine-learning model, wherein the disorder machine-learning model is trained as a function of the disorder training set.

8. The system of claim 1 , wherein determining the edible further comprises:

receiving a nourishment composition from an edible directory;

producing a nourishment demand as a function of the gestational phase; and

determining the edible as a function of the nourishment composition and the nourishment demand using the edible machine-learning model.

9. The system of claim 1 , wherein generating the nourishment program further comprises:

receiving a gestational outcome; and

generating the nourishment program as a function of the gestational outcome using a nourishment machine-learning model.

10. A method for generating a gestational disorder nourishment program, the method comprising:

obtaining, by a computing device, a maternal marker;

calculating, by the computing device, a gestational phase, wherein calculating the gestational phase further comprises:

identifying a gestational goal;

training a gestational machine-learning model using a gestational training set, wherein the gestational training set correlates a plurality of maternal markers and a plurality of gestational goals to a plurality of gestational phases;

inputting the maternal marker and the gestational goal to the trained gestational machine-learning model; and

outputting the gestational phase from the trained gestational machine-learning model as a function of the maternal marker and the gestational goal;

producing, by the computing device, a nourishment demand as a function of the gestational phase;

determining, by the computing device, an edible, wherein determining the edible further comprises:

training an edible machine-learning model using an edible training set, wherein the edible training set correlates a plurality of nutrition demands to a plurality of edibles;

inputting the nourishment demand to the trained edible machine-learning model; and

outputting the edible from the trained edible machine-learning model as a function of the nourishment demand; and

generating, by the computing device, a nourishment program as a function of the edible wherein each of the gestational machine-learning model and the edible machine-learning model is a machine-learning process selected from the group consisting of: K-nearest neighbors, support vector machines, kernel support vector machines, naive bayes, decision tree classification, random forest classification, K-means clustering, hierarchical clustering, dimensionality reduction, principal component analysis, linear discriminant analysis, kernel principal component analysis, Q-learning, State Action Reward State Action (SARSA), Deep-Q network, Markov decision processes and Deep Deterministic Policy Gradient (DDPG).

11. The method of claim 10 , wherein obtaining the maternal marker further comprises identifying an uncertainty indicator and obtaining the maternal marker as a function of the uncertainty indicator.

12. The method of claim 10 , wherein calculating the gestational phase further comprises:

determining that the maternal marker is not suitable for a first gestational phase; and

determining that the maternal marker is suitable for a second gestational phase wherein the second gestational phase occurs after the first gestational phase.

13. The method of claim 10 , wherein the method further comprises determining a gestational divergence as a function of the gestational phase and a divergence threshold.

14. The method of claim 10 , wherein calculating the gestational phase further comprises:

receiving a conception datum;

classifying the conception datum to a gestational progression level; and

calculating the gestational phase as a function of the classifying.

15. The method of claim 10 , wherein calculating the gestational phase further comprises determining a gestational disorder and producing the gestational phase as a function of the gestational disorder.

16. The method of claim 15 , wherein determining the gestational disorder further comprises:

obtaining a disorder training set that correlates at least a gestational enumeration and a gestational effect to the gestational disorder; and

determining the gestational disorder as a function of the maternal marker using a disorder machine-learning model, wherein the disorder machine-learning model is trained as a function of the disorder training set.

17. The method of claim 10 , wherein determining the edible further comprises:

receiving a nourishment composition from an edible directory;

producing a nourishment demand as a function of the gestational phase; and

determining the edible as a function of the nourishment composition and the nourishment demand using the edible machine-learning model.

18. The method of claim 10 , wherein generating the nourishment program further comprises:

receiving a gestational outcome; and

generating the nourishment program as a function of the gestational outcome using a nourishment machine-learning model.