System and method for generating a cardiovascular disease nourishment program

1. A system for generating a cardiovascular disease nourishment program, the system comprising:

a computing device, the computing device configured to:

receive at least a cardiovascular sample relating to a user;

generate at least a cardiovascular parameter as a function of the at least a cardiovascular sample;

determine a cardiovascular profile as a function of the at least a cardiovascular parameter and at least a cardiovascular deficiency, wherein:

the at least a cardiovascular deficiency is compared to a cardiovascular threshold;

the cardiovascular profile comprises a numerical cardiovascular health score correlated to the at least a cardiovascular parameter; and

the cardiovascular profile comprises an atherosclerosis indicator correlated to the at least a cardiovascular parameter, wherein the atherosclerosis indicator includes a location in a user's body wherein a vein or artery is accumulating plaque buildup;

identify at least a nutrition element as a function of the cardiovascular profile, wherein identifying comprises:

obtaining at least a nutrient composition correlated to at least a nutrition element;

determining a nourishment score as a function of the effect of the nutrition element on the cardiovascular profile; and

identifying the at least a nutrition element as a function of the nourishment score and a nutrition element machine-learning model, wherein identifying the at least a nutrition element further comprises:

training the nutrition element machine-learning model using a nutrition element training set, received as a function of user entered valuations of nourishment compositions, nourishment deficiencies, and nutrition elements and previously determined correlations of nourishment compositions and nourishment deficiencies, correlating the at least a nourishment composition and nourishment deficiency to a nutrition element, and wherein the nutrition element machine-learning model inputs nourishment compositions and nourishment deficiencies and outputs a nutrition element; and

generate a cardiovascular disease nourishment program as a function of the nourishment score, at least a classification machine-learning process, and the cardiovascular profile, wherein generating the cardiovascular disease nourishment program further comprises:

training the at least a classification machine-learning process as a function of a nourishment training set and wherein the cardiovascular disease nourishment program includes the cardiovascular health score and the atherosclerosis indicator.

2. The system of claim 1 , wherein receiving at least the cardiovascular sample comprises receiving a result of one or more tests relating the user.

3. The system of claim 1 , wherein receiving at least the cardiovascular sample comprises receiving a prior diagnosis of a cardiovascular disease relating to the user.

4. The system of claim 1 , wherein identifying the plurality of nutrition elements comprises:

classifying the cardiovascular profile to a cardiovascular disease category; and

identifying the plurality of nutrition elements according to the cardiovascular disease category.

5. The system of claim 4 , wherein identifying the plurality of nutrition elements comprises curating nutrition elements intended to prevent cardiovascular disease according to the cardiovascular disease category.

6. The system of claim 5 , wherein determining nourishment scores comprises:

generating training data using the nutrient elements identified according to the cardiovascular disease category;

training a nutrition element machine-learning model according to the training data, wherein training data includes a plurality of data entries that correlates the at least a nutrient composition for each cardiovascular disease category to nourishment score; and

determining nourishment scores as a function of the nutrition element machine-learning model and the cardiovascular profile.

7. The system of claim 1 , wherein generating the cardiovascular disease nourishment program comprises:

training a nourishment classifier as a function of a classification machine-learning process and a training set relating the at least a nutrient composition and nourishment score to nutrition elements; and

outputting the nourishment program classifier as a function of the nutrition elements, the at least a nutrient composition, and nourishment score.

8. The system of claim 1 , wherein the computing device is further configured to generate an adherence score, wherein the adherence score reflects the level of user participation in the cardiovascular nourishment program.

9. The system of claim 8 , wherein generating the adherence score comprises calculating a change in the numerical cardiovascular health score.

10. The system of claim 1 , wherein generating the cardiovascular disease nourishment program comprises:

receiving at least a user preference regarding the at least a nutrition element;

wherein the at least a user preference increases the adherence score; and

modifying the at least a nutrition element as a function of the at least a user preference.

11. A method for generating a cardiovascular disease nourishment program, the method comprising:

receiving, by a computing device at least a cardiovascular disease sample relating to a user;

generating, by the computing device, at least a cardiovascular parameter as a function of the cardiovascular sample;

determining, by the computing device, a cardiovascular profile as a function of the at least a cardiovascular parameter and at least a cardiovascular deficiency;

wherein the at least a cardiovascular deficiency is compared to a cardiovascular threshold;

wherein the cardiovascular profile comprises a numerical cardiovascular health score correlated to the at least a cardiovascular parameter;

wherein the cardiovascular profile comprises an atherosclerosis indicator correlated to the at least a cardiovascular parameter, wherein the atherosclerosis indicator includes a location in a user's body wherein a vein or artery is accumulating plaque buildup;

identifying, by the computing device, at least a nutrition element as a function of the cardiovascular profile, wherein identifying comprises:

obtaining at least a nutrient composition correlated to at least a nutrition element;

determining a nourishment score as a function of the effect of the nutrition element on the cardiovascular profile; and

identifying the at least a nutrition element as a function of the nourishment score, and nutrition element machine-learning model, wherein identifying the at least a nutrition element further comprises:

training the nutrition element machine-learning model using a nutrition element training set, received as a function of user entered valuations of nourishment compositions, nourishment deficiencies, and nutrition elements and previously determined correlations of nourishment compositions and nourishment deficiencies, correlating the at least a nourishment composition and nourishment deficiency to a nutrition element, and wherein the nutrition element machine-learning model inputs nourishment compositions and nourishment deficiencies and outputs a nutrition element; and

generating, by the computing device, a cardiovascular disease nourishment program as a function of the nourishment score, at least a classification machine-learning process, and the cardiovascular profile, wherein generating the cardiovascular disease nourishment program:

training the at least a classification machine-learning process as a function of a nourishment training set and wherein the cardiovascular disease nourishment program includes the cardiovascular health score and the atherosclerosis indicator.

12. The method of claim 11 , wherein receiving at least the cardiovascular sample comprises receiving a result of one or more tests relating the user.

13. The method of claim 11 , wherein receiving at least the cardiovascular sample comprises receiving a prior diagnosis of a cardiovascular disease relating to the user.

14. The method of claim 11 , wherein identifying the plurality of nutrition elements comprises:

classifying the cardiovascular profile to a cardiovascular disease category; and

identifying the plurality of nutrition elements according to the cardiovascular disease category.

15. The method of claim 14 , wherein identifying the plurality of nutrition elements comprises curating nutrition elements intended to prevent cardiovascular disease according to the cardiovascular disease category.

16. The method of claim 15 , wherein determining nourishment scores comprises:

generating training data using the nutrient elements identified according to the cardiovascular disease category;

training a nutrition element machine-learning model according to the training data, wherein training data includes a plurality of data entries that correlates the at least a nutrient composition for each cardiovascular disease category to nourishment score; and

determining nourishment scores as a function of the nutrition element machine-learning model and the cardiovascular profile.

17. The method of claim 11 , wherein generating the cardiovascular disease nourishment program comprises:

training a nourishment classifier as a function of a classification machine-learning process and a training set relating the at least a nutrient composition and nourishment score to nutrition elements; and

outputting the nourishment program classifier as a function of nutrition elements, the at least a nutrient composition, and nourishment score.

18. The method of claim 11 , wherein the computing device is further configured to generate the adherence score, wherein the adherence score reflects the level of user participation in the cardiovascular nourishment program.

19. The method of claim 18 , wherein generating the adherence score comprises calculating a change in the numerical cardiovascular health score.

20. The method of claim 11 , wherein generating the cardiovascular disease nourishment program comprises:

receiving at least a user preference regarding the at least a nutrition element;

wherein the at least a user preference increases the adherence score; and

modifying the at least a nutrition element as a function of the at least a user preference.