Assessing Automated Learning Models for Early Diabetes Prediction: Emphasizing Random Forest Efficiency

Diabetes is a chronic condition characterized by consistently high blood glucose levels, which, if unmanaged, can result in severe health complications such as cardiovascular disease, kidney failure, vision impairment, and in extreme cases, death.Timely identification of individuals at risk for diabetes is critical in managing the disease and preventing its progression.This paper explores the potential of ml (machine learning) models in the early phase in diabetes prediction, aiming for high classification accuracy.To achieve this, a range of machine learning algorithms were tested on the Pima Indian Diabetes dataset, which contains data on various risk factors associated with the disease.The classifiers used in the study include K-Nearest Neighbour (KNN), Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM), Gradient Boosting (GB), and Random Forest (RF).Each m2odel was evaluated for its ability to predict diabetes risk based on features provided in the dataset.The results from the experiments revealed that the Support Vector Machine achieved the highest prediction accuracy compared to the other classifiers The study highlights the promising role of machine learning in diabetes prediction and emphasizes the importance of selecting appropriate models to improve diagnostic accuracy, ultimately aiding in early intervention and better management of the condition.