 This paper examined the use of machine learning algorithms to detect and assess the risk of diabetes among individuals in Ninh Ben, Vietnam. Various classification algorithms such as Decision Tree Classifier, Logistic Regression, Support Vector Classification, Aida Boost Classifier, Gradient Boosting Classifier, Random Forest Classifier, and K Neighborhoods Classifier were used to identify the best algorithm for the dataset. The results showed that the Random Forest Classifier algorithm achieved the highest accuracy rate of 0.998 and a cross-validation score of 0.998. Additionally, the algorithm's ability to accurately predict the probability of patients developing diabetes was tested using a new dataset of 67 unseen patients. The test results revealed an accuracy rate of 94%, which demonstrates the power of machine learning algorithms in assisting clinicians with diagnosis and management. Furthermore, the study highlights the importance of leveraging machine learning in healthcare to optimize patient care and improve long-term health outcomes. This article was authored by Lin Feng Guin, Du Dinh Tung, Du Hong Tong Guin, and others. We are article.tv, links in the description below.