Performance analysis of quantum and classical machine learning models for feature selection and classification of the diabetes health ındicators dataset

Abstract

The early detection and accurate classification of diabetes health indicators are crucial for effective disease management and prevention. This study aims to compare the performance of classical and quantum machine learning models in feature selection and classification on the Diabetes Health Indicators dataset. Initially, classical machine learning methods were employed to preprocess the data, including normalization and scaling, followed by feature selection using Lasso regression. Various traditional models, such as Logistic Regression, Decision Trees, Random Forests, Gradient Boosting, K-Nearest Neighbors, and Naive Bayes, were evaluated. Among these, the Logistic Regression model achieved the highest accuracy at 85 %, while other models also demonstrated competitive performance with accuracies ranging from 82 % to 85 %. Subsequently, quantum machine learning techniques were applied using the selected features to assess their effectiveness. Quantum circuits were created using Cirq, and parameter optimization was performed through Quantum Feature Mapping and Quantum Feature Transformation. The Quantum Support Vector Machine (QSVM) model attained an accuracy of 84.33 %, showing potential for matching the performance of traditional models. The results suggest that quantum machine learning models can offer comparable accuracy to classical methods in the classification of diabetes health indicators. This study highlights the potential benefits of integrating quantum techniques in complex data processing and recommends further exploration in future research to fully harness the capabilities of quantum machine learning.