In the realm of medical diagnostics, one of the gravest challenges facing clinicians today is the early detection of chronic kidney disease (CKD). The kidney’s indispensable role in maintaining bodily homeostasis—through filtration of metabolic waste, regulation of electrolytes, and fluid balance—means that any decline in renal function can precipitate severe complications, often irreversible. CKD, a progressive condition that insidiously degrades kidney function, commonly escapes early diagnosis due to its stealthy symptomatology. Global health statistics estimate approximately 850 million individuals worldwide live with some form of renal impairment. Among this vast population, nearly 10 million patients are dependent on life-sustaining interventions such as dialysis or transplantation. Early detection remains a linchpin in curbing disease progression and ameliorating patient outcomes.
Emerging technologies in artificial intelligence (AI), particularly machine learning (ML), are transforming the landscape of medical diagnostics, offering pathways to automate and enhance disease detection accuracy. Unlike traditional diagnostic methods reliant on overt clinical manifestations or limited biomarkers, ML algorithms excel at discerning intricate, nonlinear patterns within high-dimensional biomedical datasets. These subtle signals often elude human analysis but are critical for swift and precise diagnosis. Researchers at Florida Atlantic University’s College of Engineering and Computer Science have ventured beyond conventional ML approaches by exploring the integration of quantum computing into diagnostic frameworks for CKD. Their pioneering work seeks to evaluate how quantum-enhanced machine learning may revolutionize disease prediction accuracy and computational efficiency.
At the core of this research initiative lies a comparative analysis of two diagnostic systems: a classical Support Vector Machine (CSVM) and its quantum counterpart, the Quantum Support Vector Machine (QSVM). Both methods were applied uniformly to meticulously curated datasets representative of CKD patient profiles. Preparation of these datasets involved rigorous preprocessing steps designed to eliminate noise and standardize inputs, thereby enhancing reliability. In addition, sophisticated dimensionality reduction techniques—Principal Component Analysis (PCA) and Singular Value Decomposition (SVD)—were employed to optimize feature spaces. These preprocessing algorithms play a crucial role in mitigating data redundancy, enhancing signal-to-noise ratio, and ultimately improving downstream classification performance and computational expediency.
The study’s findings, recently published in the journal Informatics and Health, unveiled insightful contrasts between the classical and quantum methodologies. When PCA was utilized for data optimization, the classical SVM attained a striking diagnostic accuracy of 98.75%, whereas the QSVM achieved a lower yet competitive accuracy of 87.5%. Using SVD, the gap widened further: CSVM achieved 96.25%, far outperforming the QSVM’s accuracy of 60%. Moreover, computational speed analyses favored the classical system markedly—CSVM was up to forty-two times faster in certain experimental contexts. These results underscore present-day hardware limitations inherent in quantum computing implementations, which currently hinder the full realization of quantum algorithmic potential in clinical diagnostics.
Despite the quantum model’s underperformance relative to its classical peer, researchers emphasize that this discrepancy is symptomatic of current quantum hardware constraints rather than a fundamental deficiency of quantum algorithms themselves. The QSVM’s 87.5% accuracy using PCA notably surpasses several classical SVM performances documented in prior studies, illustrating that even within current classical hardware simulations, quantum approaches exhibit promising diagnostic capabilities. This discovery lays the groundwork for hybrid quantum-classical computational architectures where the complementary strengths of each paradigm are leveraged in tandem. Such hybrid systems may optimize accuracy and robustness while pragmatically navigating the technological bottlenecks of early-stage quantum hardware.
“This work is unique, not only because it applies classical machine learning to chronic kidney disease diagnosis but also because it juxtaposes it directly alongside quantum methods under identical conditions,” explains Dr. Arslan Munir, the study’s senior author and associate professor at FAU’s Department of Electrical Engineering and Computer Science. Through this direct comparison combining two data-reduction techniques, the research provides an empirical benchmark that elucidates the current capacities of quantum-assisted diagnostics, offering clues on how quantum computing could augur new frontiers in healthcare analytics.
The research team acknowledges that advancing beyond QSVM to explore more sophisticated quantum machine learning algorithms represents a pivotal next step. Expanding experimental datasets to encompass diverse patient populations and integrating robust feature selection techniques will be essential for ensuring scalability and adaptability across various medical domains. The ultimate objective is to craft AI-powered diagnostic tools combining reliability, speed, and accessibility. Such tools could empower clinicians to make rapid, data-driven decisions, enhancing early-intervention strategies, and improving prognosis in chronic kidney disease and potentially other complex pathologies.
Dean Stella Batalama of the College of Engineering and Computer Science underscores the transformative potential of these innovations: “By synergizing machine learning with emergent quantum technologies, this research heralds a paradigm shift in early, rapid, and precise chronic kidney disease diagnosis. The healthcare community stands to benefit immensely from these advances—not only in CKD but across the spectrum of diseases where timely detection is critical.”
Florida Atlantic University’s multidisciplinary approach exemplifies the confluence of cutting-edge computer science, quantum physics, and clinical medicine. The College is recognized internationally for its trailblazing research, heavily supported by national agencies such as the National Science Foundation and the National Institutes of Health. Its commitment to pioneering degrees in artificial intelligence, data science, and cybersecurity aligns closely with the evolving demands of medical informatics and computational biology.
As quantum computing hardware continues to mature, overcoming current limitations in qubit coherence and error rates, studies like this one illuminate a roadmap for integrating quantum resources into routine clinical workflows. This fusion promises not merely incremental gains but potentially quantum leaps in diagnostic performance. With chronic kidney disease serving as a critical proving ground, the convergence of quantum machine learning and clinical diagnostics stands poised to fundamentally reshape the medical landscape, enhancing the early detection and management of complex diseases worldwide.
Subject of Research: People
Article Title: Performance analysis of classical and quantum support vector machines for diagnosis of chronic kidney disease
News Publication Date: 11-Sep-2025
Web References:
https://dx.doi.org/10.1016/j.infoh.2025.08.003
https://www.fau.edu/engineering/
https://www.fau.edu/
References:
Munir, A., et al. (2025). Performance analysis of classical and quantum support vector machines for diagnosis of chronic kidney disease. Informatics and Health. DOI: 10.1016/j.infoh.2025.08.003
Image Credits: Alex Dolce, Florida Atlantic University
Keywords: Artificial intelligence, Renal failure, Nephritis, Nephropathies, Machine learning, Quantum computing, Data analysis, Diagnostic accuracy, Medical diagnosis, Clinical medicine
