MINING OF NUTRITIONAL INGREDIENTS IN FOOD FOR DISEASE ANALYSIS

Abstract

In the prevention and treatment of noncommunicable illnesses,
such as cancer, it has long been recognised that a well-balanced,
nutritious diet is essential (NCDs). Research has been conducted
on the nutritional components of food that are beneficial in the
rehabilitation of noncommunicable diseases, on the other hand,
but only a small amount has been done. Because of the use of
data mining technologies, we were able to conduct a thorough
investigation into the association between food components and
illnesses. In order to get started, we compiled a list of more than
7,000 disorders, after which we decided which foods were
recommended for each condition and which foods were strict ly
forbidden. Using the China Food Nutrition as a reference, we
went on to predict which nutritional ingredients are most likely to
have beneficial impacts on disease using noise-intensity and
information entropy.
At the conclusion of the research, we proposed an improved
technique called CVNDA Red, which is based on rough sets and
is used to select the necessary core ngredients from among the
most favourable nutritional components. CVNDA Red is b a sed
on rough sets and is used to select the necessary core ngredients
from among the most favourable nutritional components. A
contraction of the phrases CVNDA and Red, which translates a s
"CVNDA Red." CVNDA Red is a trademark of the CVNDA
Corporation. According to our knowledge, this is the first
research in China to analyse the association between nutri t ious
elements in food and illnesses via the use of data mining
techniques based on rough set theory, which we believe is the
case. We have shown via experiments carried out on real -wo rld
information that our data mining technique outperforms the
conventional statistical approach, with accuracy 1.682 times
greater than the conventional statistical methodology. By way o f
aside, our research has been beneficial in uncovering the first
two to three nutritional components contained within foods tha t
may be used to aid in the rehabilitation of a range o f common
conditions such as type 2 diabetes, hypertension, and
cardiovascular disease. These experimental findings indicate the
utility of using data mining to choose nutritional components in
food for illness analysis when choosing nutritional ingredients in
food when selecting nutritional elements in food when select ing
nutritional components in food.