*2.7. Naïve Bayes*

Naïve Bayes data mining techniques help make predictions in many fields and are used by many researchers. The framework for a hybrid strategy that uses naïve Bayes for parameter optimization and genetic algorithms for prediction is presented in this research. According to the naïve Bayes model, parameters with zero values show weaknesses in the results. This problem can be resolved by applying genetic algorithm optimization. The problem 'suggested' optimizing genetic algorithms for the study. The study was initialized with an analysis of the literature on the subject of child obesity and adequate data mining models for the prediction of childhood obesity. Following the review, 19 attributes were chosen, and the NB approach was used to predict child obesity. A 75% increase in accuracy was seen in the first test to gauge the utility of the proposed approach [33].
