Abstract The paper focused on the implementation of Quantum machine learning and artificial intelligence techniques in diagnosing diseases, specifically focusing on diabetes. The paper proposed an ensemble approach that combined classical algorithms with quantum processing unit (QPU)--based algorithms to improve the performance of a model. The diabetes dataset used in the study is obtained from the (Centre for Disease Control and Prevention (CDC) repository, and the goal is to classify patients as either diabetic or non-diabetic. The ensemble algorithms examined in the study include Voting classifier, Adaboost, Xgboost, Catboost, and QPU-based Qboost. While Qboost demonstrates some quantum speedup, its performance is not satisfactory. Therefore, the proposed hybrid model is developed to enhance the performance metrics. The hybrid model achieves an average accuracy, precision, recall, f1 score, and AUC score of 0.89, 0.85, 0.95, 0.90, and 0.96, respectively, on the diabetes dataset. In comparison, the top-performing Adaboost algorithm achieves an average accuracy, precision, recall, f1 score, and AUC score of 0.94, 0.91, 0.98, 0.94, and 0.98, respectively. The paper concludes that while quantum computing (QC) significantly improves computation speed, it comes at a slight cost of a 5 \% decrease in classification metrics and 0.186 in the AUC score. Additionally, the study suggests that further development of Quantum computing hardware will enhance overall performance metrics.
Abstract The paper focused on the implementation of Quantum machine learning and artificial intelligence techniques in diagnosing diseases, specifically focusing on diabetes. The paper proposed an ensemble approach that combined classical algorithms with quantum processing unit (QPU)--based algorithms to improve the performance of a model. The diabetes dataset used in the study is obtained from the (Centre for Disease Control and Prevention (CDC) repository, and the goal is to classify patients as either diabetic or non-diabetic. The ensemble algorithms examined in the study include Voting classifier, Adaboost, Xgboost, Catboost, and QPU-based Qboost. While Qboost demonstrates some quantum speedup, its performance is not satisfactory. Therefore, the proposed hybrid model is developed to enhance the performance metrics. The hybrid model achieves an average accuracy, precision, recall, f1 score, and AUC score of 0.89, 0.85, 0.95, 0.90, and 0.96, respectively, on the diabetes dataset. In comparison, the top-performing Adaboost algorithm achieves an average accuracy, precision, recall, f1 score, and AUC score of 0.94, 0.91, 0.98, 0.94, and 0.98, respectively. The paper concludes that while quantum computing (QC) significantly improves computation speed, it comes at a slight cost of a 5 % decrease in classification metrics and 0.186 in the AUC score. Additionally, the study suggests that further development of Quantum computing hardware will enhance overall performance metrics.
This paper presents a Quantum versus classical implemented of Machine learning (ML) algorithm applied to a diabetes dataset. Diabetes is a Sixth deadliest disease in the world and approximately 10 million new cases are registered every year worldwide. Using novel Quantum computing (QC) along with Quantum Machine Learning (QML) techniques in the healthcare system to improve and accelerate the computing of existing ML models that allows the different approach to understanding the complex patterns of the disease. The proposed system tackles a binary classification problem of patients with diabetes into two different classes: diabetes patients with acute diseases and diabetes patients without acute diseases. Our study compares classical and quantum algorithms, namely Decision Tree, Random Forest, Extreme Boosting Gradient and Adaboost, Qboost, Voting Model 1, Voting Model 2, Qboost Plus, New model 1 and New Model 2 along with an ensemble method which creates a strong classifier from a committee of weak classifiers. The results we achieved using the validation metrics of the New Model 1 showed an overall precision of 69%, a recall of 69%, an F1-Score of 69%, a specificity of 69% and an accuracy of 69% on our diabetes dataset, with an increase of the computation speed by 55 times in comparison of the classical system. Our study has proved that QC improves the computational speed and its inclusion in medical applications will deliver faster results to physicians and caregivers.
This paper shows the distinction between Quantum and Classical Machine Learning techniques appeal to a diabetic mellitus dataset. Diabetes mellitus is like series of diseases that affect the body and deplete blood sugar levels (insulin). In our bodies, glucose is an important source of energy for powering mitochondria, the cells' function that make muscles and tissues strong. We use many machine learning classifiers such as Support Vector Machine, Kernel Principal Component Analysis, Bayesian Network and Decision Tree etc. These models are able predict a certain amount of data, while in case of large data there is significant amount of error and low accuracy rate, A new method known as quantum machine learning (QML) is nothing but the collaboration of machine learning in the way of quantum algorithms the term used for machine learning algorithms executed in a quantum computer for analysis of classical data is known as quantum-enhanced machine learning. In this method we are predicting diabetes mellitus using quantum machine learning algorithms which able to handle the huge data with high accuracy rate of 97% and F1-Score of 68%.Our study having implemented models of Predicted & Enhanced models.
Coronary heart disease (CHD) is a severe cardiac disease, and hence, its early diagnosis is essential as it improves treatment results and saves money on medical care. The prevailing development of quantum computing and machine learning (ML) technologies may bring practical improvement to the performance of CHD diagnosis. Quantum machine learning (QML) is receiving tremendous interest in various disciplines due to its higher performance and capabilities. Techniques for QML have the potential to forecast cardiac disease and help in early detection. To predict the risk of coronary heart disease, a hybrid approach utilising an ensemble machine learning model based on QML classifiers is presented in this paper. Our approach, with its unique ability to address multidimensional healthcare data, reassures the method’s robustness by fusing quantum and classical ML algorithms in a multi-step inferential framework. Reducing cardiac morbidity and mortality requires early detection of heart disease. In this research, a hybrid approach utilises techniques with quantum computing capabilities to tackle complex problems that are not amenable to conventional ML algorithms and to minimise computational expenses. The proposed method has been developed in the Raspberry Pi 4B Graphics Processing Unit (GPU) platform and tested on a broad dataset that integrates clinical and imaging data from patients suffering from CHD and healthy controls. The proposed research is developed with a hybrid approach that combines different machine learning algorithms, such as KNN + RF, DT + RF, LR + RF, and Adaboost + RF, for diagnosing coronary illness with higher accuracy through feature selection. The proposed system performance obtained an accuracy of 99%, utilising 20000 datasets with 14 attributes from various datasets collected from the Local Pathology Lab in the Muzaffarnagar District of Uttar Pradesh, India. Compared to classical machine learning models, the accuracy, sensitivity, F1 score, and specificity of the proposed hybrid QML model used with CHD are manifold higher.
Abstract Background Diabetes, the fastest growing health emergency, has created several life-threatening challenges to public health globally. It is a metabolic disorder and triggers many other chronic diseases such as heart attack, diabetic nephropathy, brain strokes, etc. The prime objective of this work is to develop a prognosis tool based on the PIMA Indian Diabetes dataset that will help medical practitioners in reducing the lethality associated with diabetes. Methods Based on the features present in the dataset, two prediction models have been proposed by employing deep learning (DL) and quantum machine learning (QML) techniques. The accuracy has been used to evaluate the prediction capability of these developed models. The outlier rejection, filling missing values, and normalization have been used to uplift the discriminatory performance of these models. Also, the performance of these models has been compared against state-of-the-art models. Results The performance measures such as precision, accuracy, recall, F1 score, specificity, balanced accuracy, false detection rate, missed detection rate, and diagnostic odds ratio have been achieved as 0.90, 0.95, 0.95, 0.93, 0.95, 0.95, 0.03, 0.02, and 399.00 for DL model respectively, However for QML, these measures have been computed as 0.74, 0.86, 0.85, 0.79, 0.86, 0.86, 0.11, 0.05, and 35.89 respectively. Conclusion The proposed DL model has a high diabetes prediction accuracy as compared with the developed QML and existing state-of-the-art models. It also uplifts the performance by 1.06% compared to reported work. However, the performance of the QML model has been found as satisfactory and comparable with existing literature.
To forecast heart problems, an innovative hybrid technique combining quantum computing with machine learning approaches is presented. For the preprocessing stage, normalization and dimensionality reduction techniques are used on a dataset of 270 patients' records and 13 clinical variables to make them appropriate for a 4-qubit quantum computer. Some traditional machine learning algorithms, including convolutional neural networks (CNN), support vector machines (SVM), and XGBoost, are trained to establish baselines in relation to other methods. Under the guidance of the SPSA algorithm, a quantum-inspired classifier called VQC is developed using a parameterized quantum circuit that includes rotation and entanglement operations. A quantitative study of crucial performance metrics reveals that the suggested quantum technique outperforms conventional methods in tests. The VQC classifier has the highest accuracy (85.2%), precision (86.4%), and F1 score (82.6%), while matching the SVM classifier's ROC-AUC score of 88.9%. Although with lower dimensionality, this research shows how quantum-based machine learning may find complex correlations among variables. This study is intended to serve as a preliminary concept for using quantum machine learning for healthcare applications, especially for increasing the accuracy rate and lowering misdiagnosis errors.
Early diagnosis of chronic diseases is paramount to improving patient survival rates, optimizing treatment trajectories, and mitigating escalating healthcare expenditures. While the recent explosion of heterogeneous healthcare data presents robust opportunities for predictive modeling, it simultaneously introduces severe computational bottlenecks regarding data dimensionality and non-linear complexity. This paper proposes a novel, high-performance hybrid prediction framework that seamlessly fuses classical machine learning topologies with Variational Quantum Circuits (VQCs) within a Quantum Machine Learning (QML) paradigm. The proposed architecture targets the multi-classification and risk profiling of five high-prevalence chronic conditions: diabetes, cardiovascular disease, breast cancer, chronic liver disease, and chronic kidney disease This project introduces “Quantum Health,” a Quantum Machine Learning (QML)-based multi disease prediction system that integrates classical and quantum computing to enhance diagnostic performance. It employs five quantum-enhanced models: Quantum Random Forest for breast cancer, Quantum Neural Network for diabetes, Quantum k-Nearest Neighbors (QkNN) for heart disease, Quantum AdaBoost for liver disease, and Quantum Naive Bayes for kidney disease. The models are evaluated using both classical methods and PennyLane simulations, enabling direct comparison of performance. The system is deployed as a Flask-based web application featuring a user-friendly dashboard, disease-specific prediction modules, prediction history with CSV export, and an analytics dashboard comparing classical and quantum accuracies. Each module provides predictions, confidence scores, and feature importance insights. Unlike monolithic classical models, our approach incorporates a parameterized quantum layer that maps low-dimensional classical medical vectors into high-dimensional Hilbert spaces, capturing intricate, non-linear correlations that evade classical kernels. Experimental evaluations executed on simulated quantum backends demonstrate that the proposed Quantum Hybrid Model drastically outperforms conventional baselines, achieving an overall classification accuracy of 96.8% and an Area Under the Curve (AUC) of 98.4%. The findings establish that integrating quantum circuit state vectors with ensemble classical architectures provides a scalable, computationally efficient, and highly generalizable paradigm for next-generation clinical decision support systems.