Abstract
The main issue is undocumented migration, it presents a significant concern to public health due to the potential spread of infectious illnesses and overall health risks. Data was gathered between January 2018 and December 2022. The present study employs contemporary technology methodologies, including big data analytics and machine learning, to better understand health risks and the dissemination patterns of prominent diseases among undocumented immigrants. The main infectious diseases, included Tuberculosis, hepatitis, and chronic disorders. Interpretation: The study findings indicate a greater likelihood of specific contagious diseases among undocumented immigrants. The applied machine learning models successfully obtained diseases health risk information. According to our experimental analysis, the Convolutional Neural Network and K-Nearest Neighbors model achieved the highest performance with 90% Precision, 90% Recall, and 80% F1-Score, respectively, compared to other machine learning techniques - Logistic Regression (80% Precision, 80% Recall, and a 70% F1-Score), Decision Tree (70% Precision, 60% Recall, 70% F1-Score), and Naïve Bayes (50% Precision, 80% Recall, and 60% F1-Score). The study will be helpful in promoting scrutiny in analyzing public health records and understanding of diseases associated with undocumented immigrants in the U.S.
