Diabesta Faction Security in Machine Learning

Diabetes is a disease that could impact high levels of glucose in the human body. It should not be ignored until proper treatment is administered with proper precautions, sometimes due to irresponsibility assumed by patients, leading to heart problems, kidney problems, blood pressure, lesions eyes and may affect other organs of the human body. If precautions are taken from the beginning, it can be cured. In the proposed work machine learning classification and defined techniques on a dataset to predict diabetes is being done. Such as Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM) and Random Forest (RF). The end result shows that Random Forest achieved higher accuracy than other machine learning techniques. The importance of privacy in deep learning applications is directly related to the emergence of distributed and multiparty models. Keywords: Machine learning, prediction, security, differential privacy, random forest, logistic regression, super vector machine.