[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125472-en":3,"doc-seo-125472-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125472,1099514068365,"Aurelia","https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?_k=1776742907772140068",8,"Research & Report","Quantum Machine Learning in Healthcare - Evaluating QNN and QSVM Models","Effective and accurate diagnosis of diseases such as cancer, diabetes, and heart failure is crucial for timely medical intervention and improved patient survival. Machine learning diagnosis relies on classification models built from selected patient features, yet highly imbalanced datasets often limit classical performance by producing biased predictions. Quantum models may address this by using superposition and entanglement to learn complex patterns in higher-dimensional spaces. The study evaluates QNNs and QSVMs against classical baselines using Prostate Cancer, Heart Failure, and Diabetes datasets.","POLITECNICO DI TORINO  \nRepository ISTITUZIONALE  \nQuantum Machine Learning in Healthcare: Evaluating QNN and QSVM Models  \nOriginal  \nQuantum Machine Learning in Healthcare: Evaluating QNN and QSVM Models / Tudisco, Antonio; Volpe, Deborah; Turvani, Giovanna. - (2025), pp. 1-8. ( 2025 International Joint Conference on Neural Networks (IJCNN) Roma (ita) 30 June 2025-05 July 2025) [10 . 1109/ijcnn64981 .2025. 11227750] .  \nAvailability:  \nThis version is available at: 11583/3005190 since: 2025-11-17T08:55:00Z  \nPublisher: IEEE  \nPublished  \nDOI:10.1109/ijcnn64981.2025.11227750  \nTerms of use:  \nThis article is made available under terms and conditions as specified in the corresponding bibliographic description in the repository  \nPublisher copyright  \nIEEE postprint/Author's Accepted Manuscript  \n©2025 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collecting works, for resale or lists, or reuse of any copyrighted component of this work in other works.  \n(Article begins on next page)  \n21 February 2026  \nQuantum Machine Learning in Healthcare: Evaluating QNN and QSVM Models  \nAntonio Tudisco∗ , Deborah Volpe∗ , and Giovanna Turvani∗  \n∗Department of Electronics and Telecommunications, Politecnico di Torino Italy  \nantonio.tudisco@polito.it, deborah.volpe@polito.it, and giovanna.turvani@polito.it  \nAbstract—Effective and accurate diagnosis of diseases such as cancer, diabetes, and heart failure is crucial for timely medical intervention and improving patient survival rates. Machine learning has revolutionized diagnostic methods in recent years by developing classification models that detect diseases based on selected features. However, these classification tasks are often highly imbalanced, limiting the performance of classical models. Quantum models offer a promising alternative, exploiting their ability to express complex patterns by operating in a higherdimensional computational space through superposition and entanglement. These unique properties make quantum models potentially more effective in addressing the challenges of imbalanced datasets. This work evaluates the potential of quantum classifiers in healthcare, focusing on Quantum Neural Networks (QNNs) and Quantum Support Vector Machines (QSVMs), comparing them with popular classical models. The study is based on three well-known healthcare datasets—Prostate Cancer, Heart Failure, and Diabetes.  \nThe results indicate that QSVMs outperform QNNs across all datasets due to their susceptibility to overfitting. Furthermore, quantum models prove the ability to overcome classical models in scenarios with high dataset imbalance. Although preliminary, these findings highlight the potential of quantum models in healthcare classification tasks and lead the way for further research in this domain.  \nIndex Terms—Quantum Neural Network, Variational Quantum Circuits, Quantum Support Vector Machines, Healthcare, Classification  \nI. INTRODUCTION  \nEffective and accurate diagnosis of diseases such as cancer, diabetes, and heart failure is crucial for timely medical intervention and improving patient survival rates. In recent years, Machine Learning (ML) has revolutionized diagnostic methods by developing classification models [1] that detect diseases based on selected features derived from patient data, such as biomarkers, genetic profiles, and medical imaging. These methods exploit algorithms ranging from logistic regression and support vector machines, enabling automated systems to identify patterns that may be difficult for human experts to distinguish. However, these classification tasks are often highly imbalanced, as the prevalence of the disease is much lower than that of healthy cases or vice versa in the datasets used for training. This imbalance can lead to biased models that favor the ","cbCaithjSLT0XGEM","https://ap.wps.com/l/cbCaithjSLT0XGEM","pdf",500131,1,9,"English","en",105,"# Introduction\n## Healthcare diagnosis and data imbalance\n## Classical machine learning approaches\n## Quantum machine learning for improved representations\n## QNNs and QSVMs overview","[{\"question\":\"Why are healthcare classification datasets often challenging for classical machine learning models?\",\"answer\":\"They are frequently highly imbalanced, so models tend to favor the majority class, reducing sensitivity and recall for the minority (disease) class.\"},{\"question\":\"What quantum properties motivate the use of quantum models in healthcare diagnostics?\",\"answer\":\"Quantum models exploit superposition and entanglement to represent complex patterns in higher-dimensional computational spaces, supporting more expressive feature learning.\"},{\"question\":\"How do QNN and QSVM models compare in the reported evaluation?\",\"answer\":\"QSVMs outperform QNNs across all datasets, attributed to QNNs being more susceptible to overfitting. Quantum models also help in scenarios with high dataset imbalance.\"}]","Quantum Machine Learning in Healthcare - Evaluating QNN and QSVM Models | PDF",1785899187,23,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"quantum-machine-learning-in-healthcare-evaluating-qnn-and-qsvm-models","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/quantum-machine-learning-in-healthcare-evaluating-qnn-and-qsvm-models/125472/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why are healthcare classification datasets often challenging for classical machine learning models?","Question",{"text":75,"@type":76},"They are frequently highly imbalanced, so models tend to favor the majority class, reducing sensitivity and recall for the minority (disease) class.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What quantum properties motivate the use of quantum models in healthcare diagnostics?",{"text":80,"@type":76},"Quantum models exploit superposition and entanglement to represent complex patterns in higher-dimensional computational spaces, supporting more expressive feature learning.",{"name":82,"@type":73,"acceptedAnswer":83},"How do QNN and QSVM models compare in the reported evaluation?",{"text":84,"@type":76},"QSVMs outperform QNNs across all datasets, attributed to QNNs being more susceptible to overfitting. 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