Comparison and selection of machine learning algorithms for diabetes prediction: An exploratory quantitative study based on medical data analysis

Abstract

The global prevalence of diabetes is increasing at an alarming rate, making early and accurate detection a critical area of interest. This study employs Machine Learning techniques to predict the incidence of diabetes in a population of women from the Pima heritage, known for their predisposition to the disease. Using a database of diagnostic measures, multiple algorithms were applied, including Support Vector Machines (SVM), Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), Decision Trees, and Random Forest, to develop predictive models. Principal Component Analysis (PCA) was implemented for dimensionality reduction and highlighting of key diagnostic variables, optimizing algorithm performance. The results highlighted the superior- ity of the Random Forest, which showed higher accuracy and precision, suggesting its viability as a clinical diagnostic tool. This study contributes to the emerging field of artificial intelligence ap- plications in health, providing valuable insights for the prevention and early treatment of diabetes.

DOI:https://doi.org/10.56238/sevened2024.007-053