A Study on Data Analysis and Prediction of Diabetes Using Machine Learning Models

Abstract

It's crucial to emphasize that although these approaches may aid in diabetes prediction, it is essential for a healthcare provider to make the diagnosis and offer guidance on managing the condition. While AI and machine learning-based diabetes prediction models are advancing in accuracy and sophistication, they should complement medical expertise as supportive tools. Data mining is typically described as the practice of employing computer systems and automation to explore extensive datasets, identifying patterns and trends, and converting these discoveries into valuable business insights and predictive analyses. This paper considers diabetes prediction-related dataset data like gender, age, hypertension, heart disease, smoking history, bmi, HbA1c level, Blood Glucose level, diabetes. The machine learning approaches which is used to analysis and predict the dataset using linear regression, decision stump, M5P, random forest, random tree, and REP tree. Numerical illustrations are provided to prove the proposed results with test statistics or accuracy parameters.