Schizophrenia is a complex and debilitating mental disorder characterized by hallucinations, delusions, and cognitive impairments. Early detection and prediction are essential for effective intervention and improved patient outcomes. However, conventional machine learning methods face challenges in handling large, high-dimensional psychiatric datasets. To overcome these limitations, this study proposes a Quantum Machine Learning (QML)–based approach for schizophrenia prediction. By leveraging the computational capabilities of quantum computing, QML offers advanced techniques for analyzing complex data structures beyond the capacity of classical algorithms. This study evaluates the strengths and limitations of various quantum approaches and highlights appropriate evaluation metrics and validation strategies tailored for QML models. The findings provide a novel perspective on computational mental health diagnostics and underscore the potential of quantum computing in psychiatric prediction.
Schizophrenia is a complex and debilitating mental disorder characterized by hallucinations, delusions, and cognitive impairments. Early detection and prediction are essential for effective intervention and improved patient outcomes. However, conventional machine learning methods face challenges in handling large, high-dimensional psychiatric datasets. To overcome these limitations, this study proposes a Quantum Machine Learning (QML)–based approach for schizophrenia prediction. By leveraging the computational capabilities of quantum computing, QML offers advanced techniques for analyzing complex data structures beyond the capacity of classical algorithms. This study evaluates the strengths and limitations of various quantum approaches and highlights appropriate evaluation metrics and validation strategies tailored for QML models. The findings provide a novel perspective on computational mental health diagnostics and underscore the potential of quantum computing in psychiatric prediction.
Globally, mental health diseases have a substantial influence on people, necessitating the urgent need for novel and precise diagnostic techniques. The power of quantum machine learning (QML) classifiers to accurately predict mental health disorders is examined in this study. QML promises promising improvements over conventional techniques by utilizing the special powers of quantum computing, such as increased processing speed and the capacity to handle complicated and highdimensional information. The study compares the effectiveness of classical algorithms with quantum classifiers, including quantum support vector machines and quantum neural networks. Through the use of several real-world datasets that include demographic, behavioral, and diagnostic variables, the study investigates the classifiers' accuracy, scalability, and noise resistance. The findings show that QML classifiers are particularly good at spotting complex and nuanced patterns in mental health data. Potential solutions are addressed along with hardware limits and quantum noise challenges. This study opens the door for further advancements in the field by demonstrating the potential of quantum technologies to support data-driven, individualized mental health care.
The increasing cases of mental illnesses such as stress, anxiety, and depression, driven by excessive workload, social pressure, and emotional instability, highlight the need for early and accurate detection. Traditional Machine Learning (ML) and Deep Learning (DL) models were initially used for this purpose, as they can analyse psychological datasets like DASS-42 and provide automated mental health assessment. However, these classical models face challenges in capturing complex emotional patterns and high-dimensional feature interactions, which limits their overall diagnostic efficiency. To overcome these limitations, Quantum Machine Learning (QML) was introduced. In this article, we applied the Variational Quantum Classifier (VQC) model that leverages quantum properties such as superposition and entanglement, allowing it to process and distinguish subtle mental health indicators more effectively than ML and DL approaches. The Quantum model (VQC) outperformed all classical approaches, delivering an exceptional accuracy of 99.95%, with precision, recall, and F1-score values nearly reaching 1.0, indicating highly reliable classification consistency. A comparative evaluation revealed that the quantum model demonstrated superior classification capabilities, outperforming classical systems in consistency and predictive reliability. This work establishes QML as a promising direction toward next-generation mental health diagnosis systems.
Mental health disorders represent a growing global healthcare challenge affecting hundreds of millions of people worldwide. Major depressive disorder (MDD), anxiety disorders, post-traumatic stress disorder (PTSD), and Alzheimer’s disease place huge pressure on medical systems. A central challenge is early detection, which remains difficult due to the complexity of symptoms related to these diseases. Delayed diagnosis is also due to limited access to specialists and the multimodal complexity of relevant biomarkers, clinical assessments, neuroimaging, physiological signals, and behavioral data. Recent developments in artificial intelligence (AI) have demonstrated promising results in improving diagnostic support. However, classical AI still struggles to model high-dimensional correlations and inherent uncertainties that are common to cognitive-emotional data. This chapter explores quantum machine learning (QML) as a transformative paradigm to overcome these limitations. We provide a comprehensive overview of foundational QML architectures and evaluate its suitability for processing diverse healthcare data such as neuroimaging, wearable sensor inputs, and electronic health records (EHRs). We propose a new conceptual framework for a quantum-enabled clinical decision support system that integrates multimodal feature extraction. Taking into consideration the sensitivity of psychiatric data, we critically examine security imperatives that include federated learning and post-quantum privacy protection. This chapter aims to provide researchers and practitioners with a comprehensive perspective on how quantum computing (QC) may contribute to future intelligent mental healthcare.
Schizophrenia is a serious chronic mental disorder that significantly affects daily life. Electroencephalography (EEG), a method used to measure mental activities in the brain, is among the techniques employed in the diagnosis of schizophrenia. The symptoms of the disease typically begin in childhood and become more pronounced as one grows older. However, it can be managed with specific treatments. Computer-aided methods can be used to achieve an early diagnosis of this illness. In this study, various machine learning algorithms and the emerging technology of quantum-based machine learning algorithm were used to detect schizophrenia using EEG signals. The principal component analysis (PCA) method was applied to process the obtained data in quantum systems. The data, which were reduced in dimensionality, were transformed into qubit form using various feature maps and provided as input to the Quantum Support Vector Machine (QSVM) algorithm. Thus, the QSVM algorithm was applied using different qubit numbers and different circuits in addition to classical machine learning algorithms. All analyses were conducted in the simulator environment of the IBM Quantum Platform. In the classification of this EEG dataset, it is evident that the QSVM algorithm demonstrated superior performance with a 100% success rate when using Pauli X and Pauli Z feature maps. This study serves as proof that quantum machine learning algorithms can be effectively utilized in the field of healthcare.
Quantum machine learning (QML) in the field of disease detection and prediction use quantum computing techniques and algorithms to analyze and classify large datasets of medical information, by identifying subtle patterns and predict the occurrence or progression of diseases. It involves applying machine learning techniques to data from biological and medical research, such as-genomic and proteomic data, medical imaging, electronic health records, and clinical trial data, using quantum computing algorithms and architectures to perform these analyses more efficiently and accurately than classical computing methods. This approach has the potential to provide new insights into complex biological systems and facilitate the development of more effective treatments and personalized medicine. In this paper, a systematic review of the use of QML algorithms has been conducted, which focuses on the detection and prediction of diseases among patients. The current essence of the field along with the challenges and limitations of current works have also been discussed. After evaluating the implemented and proposed methods of data analysis, algorithm development, usefulness and efficiency of the system in various disease detection and prediction, a recommendation was made on the open research scopes in this field at the end of the paper.
Prompt diagnosis of disease is a strong driver for decreasing mortality, improving patient care and healthcare expenditures. Classical machine learning (ML), which has shown great potential in bolstering predictive modeling throughout oncology, cardiology, and neurology, runs the risk of excessive data dimensionality along with high computational complexity. Quantum computing is an exciting new paradigm for computation that takes advantage of superposition and entanglement which can be beneficial in learning patterns in high-dimensional spaces and for kernel-based learning. This review provides a systematic summary of the preliminary work on quantum machine learning (QML) with empirical applications to early disease diagnosis, while emphasizing its theoretical foundations, algorithmic developments and application in healthcare. We scope the conversation on quantum support vector machines, variational quantum classifiers, quantum kernels and hybrid architectures for a quantum classical. We also examine current hardware limitations in the NISQ era and assess how close these methods are to being ready for translation into public health applications. Although widespread clinical application is still far away, hybrid QML approaches are finding utility in low-data and high-complexity settings. We conclude with a discussion of research challenges, regulation and strategy towards realizing such quantum-enhanced precision diagnostics through U.S. public health innovation initiatives