Acquired Immunodeficiency Syndrome (AIDS) caused by the Human Immunodeficiency Virus (HIV) is one of the deadliest infectious diseases in the world. Understanding its spread and epidemiological characteristics is crucial for developing and preventing more effective treatments. This study uses the K-Medoids method with a quantum computing approach to predict AIDS based on clinical and demographic data. K-Medoids is chosen to group large amounts of data using a clustering technique that determines the center point (medoid) of each cluster, minimizing the overall distance between data in a cluster. The Manhattan distance is used because it is easier to process data. The quantum computing approach is used to overcome the limitations of classical computing when processing large-scale medical data. This study shows that the application of quantum algorithms to the K-Medoids method allows for faster and more accurate predictions in the diagnosis of AIDS. The tests carried out showed that the prediction accuracy of classical and quantum methods was comparable, namely 85%. The results support the great potential of quantum computing to improve the efficiency of medical predictions. The research involves converting data into quantum format, processing it with the K-Medoids algorithm, and evaluating its performance based on metrics such as intercluster distance and computation time. The research will also identify patterns and risk factor for the spread of AIDS that can be used to develop more effective health interventions. The conclusion of the research is that integrating the K-Medoids techniques can only increase the speed of data processing but also provide competitive accuracy compared to traditional techniques. This research opens up new possibilities in medical data analysis, especially when managing large and complex data sets. The bottom line is that these findings can help make better medical decisions and strategically support AIDS prevention and treatment efforts.Acquired Immunodeficiency Syndrome (AIDS) caused by the Human Immunodeficiency Virus (HIV) is one of the deadliest infectious diseases in the world. Understanding its spread and epidemiological characteristics is crucial for developing and preventing more effective treatments. This study uses the K-Medoids method with a quantum computing approach to predict AIDS based on clinical and demographic data. K-Medoids is chosen to group large amounts of data using a clustering technique that determines the center point (medoid) of each cluster, minimizing the overall distance between data in a cluster. The Manhattan distance is used because it is easier to process data. The quantum computing approach is used to overcome the limitations of classical computing when processing large-scale medical data. This study shows that the application of quantum algorithms to the K-Medoids method allows for faster and more accurate predictions in the diagnosis of AIDS. The tests carried out showed that the prediction accuracy of classical and quantum methods was comparable, namely 85%. The results support the great potential of quantum computing to improve the efficiency of medical predictions. The research involves converting data into quantum format, processing it with the K-Medoids algorithm, and evaluating its performance based on metrics such as intercluster distance and computation time. The research will also identify patterns and risk factor for the spread of AIDS that can be used to develop more effective health interventions. The conclusion of the research is that integrating the K-Medoids techniques can only increase the speed of data processing but also provide competitive accuracy compared to traditional techniques. This research opens up new possibilities in medical data analysis, especially when managing large and complex data sets. The bottom line is that these findings can help make better medical decisions and strategically support AIDS prevention and treatment efforts.

ILKOM Jurnal IlmiahApr 20, 20250 citations

Quantum Approaches for Medoid Clustering

Thom Sijpesteijn, Frank Phillipson

Communications in computer and information scienceJan 1, 20232 citations

Design and Evaluation of a Quantum-Enabled Explainable AI System for Disease Risk Prediction in Healthcare

Dattatray Raghunath Kale, Panil Jain, Tejal Deshpande +3 more

Disease hazard prediction has been transformed by the use of artificial intelligence in healthcare; however, there are still problems with striking a balance between interpretability and predictive routine, mostly when working with complex, high-dimensional clinical data. To improve the precision, scalability, and transparency of disease risk prediction, this work presents a novel Quantum-Enabled Explainable AI (QXAI) system that syndicates explainable machine learning with quantum computing. Through the use of quantum algorithms, explicitly quantum kernel methods and quantum-enhanced feature selection, the suggested system achieves huge electronic health record (EHR) datasets efficiently while addressing problems with non-linearity and data narrowness. This work uses explainable AI methods like SHAP and counterfactual reasoning to guarantee interpretability, allowing doctors to understand the reasoning behind predictions. Real-world medical datasets are used for a thorough evaluation, which shows that the QXAI system finishes better than traditional baselines in terms of prediction accuracy, model robustness, and explanation reliability. In the end, this paper places the foundation for the implementation of reliable, quantum-assisted decision support systems in medical practice, which will lead to more personalized, reasonable, and knowledgeable healthcare.Disease hazard prediction has been transformed by the use of artificial intelligence in healthcare; however, there are still problems with striking a balance between interpretability and predictive routine, mostly when working with complex, high-dimensional clinical data. To improve the precision, scalability, and transparency of disease risk prediction, this work presents a novel Quantum-Enabled Explainable AI (QXAI) system that syndicates explainable machine learning with quantum computing. Through the use of quantum algorithms, explicitly quantum kernel methods and quantum-enhanced feature selection, the suggested system achieves huge electronic health record (EHR) datasets efficiently while addressing problems with non-linearity and data narrowness. This work uses explainable AI methods like SHAP and counterfactual reasoning to guarantee interpretability, allowing doctors to understand the reasoning behind predictions. Real-world medical datasets are used for a thorough evaluation, which shows that the QXAI system finishes better than traditional baselines in terms of prediction accuracy, model robustness, and explanation reliability. In the end, this paper places the foundation for the implementation of reliable, quantum-assisted decision support systems in medical practice, which will lead to more personalized, reasonable, and knowledgeable healthcare.

IEEE International Conference on Blockchain and Distributed Systems Security (ICBDS)Oct 30, 20252 citations

Quantum machine learning in medical image analysis: A survey

Wei Ting Lin, Haowen Liu, Jing Xu +4 more

NeurocomputingJan 11, 2023134 citations

Quantum-Based Data Structure for Medical Image Encoding

Md Sarwar Kamal, Sonia Farhana Nimmy, Linkon Chowdhury +1 more

Medical image encoding has several challenges. File sizes are large. Processing is slow. Fine details like edges, textures, and soft tissue patterns can be lost. Traditional methods use matrix operations that are powerful but often hard to scale and interpret. These limitations highlight opportunities to improve image encoding with better interpretability. To explore new opportunities for scalable image encoding, we introduce a quantum inspired model built on a dictionary structure. In this model, quantum states are stored as key value pairs for ensuring interpretability and scalability. We do not directly implement quantum gates. we provide dictionary based alternatives that emulate gate behavior using key value logic. We simulate the effect of quantum gates on these structures. For example, a Hadamard like rule may help capture sharp or high contrast areas. Phase like rules (such as $S$ and $T$ ) may respond to smooth or soft changes. Control rules, like $C X$ and $C C X$ , can connect different parts of the image using logic. It may reduce the number of steps needed for image operations. This makes it useful for small quantum image datasets. It also makes the steps more explainable. The process is also traceable through the dictionary logic. We tested this method on grayscale MRI image patches. It performs well in tasks such as image compression. It also supports pattern recognition and secure image sharing. These results suggest its suitability for medical image applications. The model balances technical performance and explainability. This makes it helpful for developers building quantum tools. It also supports researchers and students in medical image analysis.Medical image encoding has several challenges. File sizes are large. Processing is slow. Fine details like edges, textures, and soft tissue patterns can be lost. Traditional methods use matrix operations that are powerful but often hard to scale and interpret. These limitations highlight opportunities to improve image encoding with better interpretability. To explore new opportunities for scalable image encoding, we introduce a quantum inspired model built on a dictionary structure. In this model, quantum states are stored as key value pairs for ensuring interpretability and scalability. We do not directly implement quantum gates. we provide dictionary based alternatives that emulate gate behavior using key value logic. We simulate the effect of quantum gates on these structures. For example, a Hadamard like rule may help capture sharp or high contrast areas. Phase like rules (such as$S$and$T$) may respond to smooth or soft changes. Control rules, like$C X$and$C C X$, can connect different parts of the image using logic. It may reduce the number of steps needed for image operations. This makes it useful for small quantum image datasets. It also makes the steps more explainable. The process is also traceable through the dictionary logic. We tested this method on grayscale MRI image patches. It performs well in tasks such as image compression. It also supports pattern recognition and secure image sharing. These results suggest its suitability for medical image applications. The model balances technical performance and explainability. This makes it helpful for developers building quantum tools. It also supports researchers and students in medical image analysis.

IEEE ... International Conference on Data Mining workshopsNov 12, 20251 citation

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, can connect different parts of the image using logic. It may reduce the number of steps needed for image operations. This makes it useful for small quantum image datasets. It also makes the steps more explainable. The process is also traceable through the dictionary logic. We tested this method on grayscale MRI image patches. It performs well in tasks such as image compression. It also supports pattern recognition and secure image sharing. These results suggest its suitability for medical image applications. The model balances technical performance and explainability. This makes it helpful for developers building quantum tools. It also supports researchers and students in medical image analysis.