No abstract

Artificial intelligence's use in health systems has evolved substantially in recent years. In medical diagnosis, machine learning (ML) has a wide variety of uses. Machine learning techniques are used to forecast or diagnose a variety of life-threatening illnesses, including cancer, diabetes, heart disease, thyroid disease, and so on. Chronic diabetes is one of the most common diseases worldwide and making the diagnosis process simpler and quicker would have a huge effect on the treatment process. The fundamental goal of this work is to prepare and carry out diabetes prediction using various machine learning techniques and Conduct output analysis of those techniques to find the best classifier with the highest accuracy. This study examines diabetes prediction by taking different diabetes disease-related attributes. We use the Pima Indian Diabetes Dataset and applied the Machine Learning classification methods like K-Nearest Neighbors (KNN), Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Network (ANN), and Decision Tree (DT) for diabetes prediction. The models used in this analysis have various degrees of accuracy. This study shows a model that can correctly forecast diabetes. In comparison to other machine learning methods, the random forest has high accuracy in forecasting diabetes, according to the findings of this study.