Comparative Analysis of Machine Learning Algorithms for Early Detection of Diabetes

Abstract

Diabetes is a common health problem that affects people in many parts of the world. Detecting it early is important because timely action can help control the condition and reduce the risk of serious complications. Traditionally, diabetes is diagnosed through clinical examination and laboratory tests. With the growing amount of healthcare data available, machine learning can also be used to find patterns that may help identify people who are at risk. This paper compares four machine learning algorithms for the early detection of diabetes: Logistic Regression, Support Vector Machine (SVM), Random Forest, and XGBoost. The general process includes data preprocessing, feature preparation, model development, and performance evaluation. The models can use patient information such as glucose level, blood pressure, body mass index, age, and insulin level. Their performance can be assessed using accuracy, precision, recall, F1-score, and ROC-AUC. Each method has its own strengths. Logistic Regression is easy to understand, SVM is useful for more complex classification problems, Random Forest can handle nonlinear relationships, and XGBoost is a powerful method for structured data.