Immune-Based Nutrition Deficiency Analysis Using Classification Algorithms

Nutrition deficiency poses a significant global health issue, impacting immune functionality and overall wellness. Recognizing the intricate nexus between nutrition and immune well-being stands crucial for devising efficacious intervention tactics. This investigation delves into utilizing classification algorithms to scrutinize deficiencies in nutrition with immune ramifications. Employing feature engineering strategies, it extracts pertinent features, encompassing counts of White Blood Cells, Neutrophils, Lymphocytes, and Immune cells. According to the Immunity, this categorizes the dataset. Various classification algorithms, including Random Forest, Decision Tree, and Naive Bayes, are employed to construct predictive models. The efficacy of each model is gauged using performance metrics such as Accuracy, Precision, Recall, and F1-score. By comparing the model’s performance, the Random Forest algorithm provides better accuracy with a rate of 99.4. Thus, utilizing this, it can offer suggestions for nutritional content consumption based on an individual’s immune status.