Introducing HeliEns: A Novel Hybrid Ensemble Learning Algorithm for Early Diagnosis of Helicobacter pylori Infection

Abstract

The Gram-negative bacterium Helicobacter pylori (H. infection) infects the human stomach and is a major cause of gastritis, peptic ulcers, and gastric cancer. With over 50% of the global population affected, early and accurate diagnosis of H. infection infection is crucial for effective treatment and prevention of severe complications. Traditional diagnostic methods, such as endoscopy with biopsy, serology, urea breath tests, and stool antigen tests, are often invasive, costly, and can lack precision. Recent advancements in machine learning (ML) and quantum machine learning (QML) offer promising non-invasive alternatives capable of analyzing complex datasets to identify patterns not easily discernible by human analysis. This research aims to develop and evaluate HeliEns, a novel quantum hybrid ensemble learning algorithm designed for the early and accurate diagnosis of H. infection infection. HeliEns combines the strengths of multiple quantum machine learning models, specifically Quantum K-Nearest Neighbors (QKNN), Quantum Naive Bayes (QNB), and Quantum Logistic Regression (QLR), to enhance diagnostic accuracy and reliability. The development of HeliEns involved rigorous data preprocessing steps, including data cleaning, encoding of categorical variables, and feature scaling, to ensure the dataset’s suitability for quantum machine learning algorithms. Individual models (QKNN, QNB, and QLR) were trained and evaluated using metrics such as accuracy, precision, recall, and F1-score. The ensemble model was then constructed by integrating these quantum models using a hybrid approach that leverages their diverse strengths. The HeliEns model demonstrated superior performance compared to individual models, achieving an accuracy of 94%, precision of 97%, recall of 92%, and an F1-score of 94% in detecting H. infection infection. The quantum ensemble approach effectively mitigated the limitations of individual models, providing a robust and reliable diagnostic tool. HeliEns significantly improved diagnostic accuracy and reliability for early H. infection detection. The integration of multiple quantum ML algorithms within the HeliEns framework enhanced overall model performance. The non-invasive nature of the HeliEns model offers a cost-effective and user-friendly alternative to traditional diagnostic methods. This research underscores the transformative potential of quantum machine learning in healthcare, particularly in enhancing diagnostic efficiency and patient outcomes. HeliEns represents a significant advancement in the early diagnosis of H. infection infection, leveraging quantum machine learning to provide a non-invasive, accurate, and reliable diagnostic tool. This research highlights the importance of QML-driven solutions in healthcare and sets the stage for future research to further refine and validate the HeliEns model in real-world clinical settings.