Construction of Food Safety Tracebility Mechanism Using Random Forest Rule Based Algorithm
Keywords:
Data mining, Rule Based Random Forest Algorithm, Non-Communicable DiseasesAbstract
It is commonly acknowledged that eating a diet high in nutrients can help prevent and manage non-communicable diseases (NCDs). Currently, there is a dearth of study on food elements that are nutritive and helpful in the rehabilitation of non-communicable diseases. In this effort, the data mining techniques are used to thoroughly examine the connection between dietary components and illnesses. First, we gathered the foods that were banned and advised for each of the more than n ailments that were identified. Experiments conducted on real-world data demonstrate that our data-mining strategy outperforms the conventional statistical method in terms of performance. We can help medical professionals and disease investigators identify the best nutritional components that support the recovery of various illnesses as precisely as we can. Certain data are currently unavailable because they are awaiting medical verification. The uploaded dataset will undergo pre-processing, feature extraction, noisy data removal, and classification using a rule based random forest algorithm. Based on this study, the individual's food intake will be predicted to cause the disease.
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