https://journals.tultech.eu/index.php/jtse <p><strong>Journal of Transactions in Systems Engineering (JTSE)</strong> is an open-access and peer-reviewed journal that provides the latest research and developments in all theoretical and practical aspects and fields of engineering applications, informatics, and engineering systems design. The journal publishes three times a year (January, June, and October). All the content is freely available without charge to the user or his/her institution. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles, or use them for any other lawful purpose, without asking prior permission from the publisher or the author. This is in accordance with the DOAJ and BOAI definition of open access..</p> TULTECH en-US Journal of Transactions in Systems Engineering 2806-2973 https://journals.tultech.eu/index.php/jtse/article/view/395 <p style="font-weight: 400;">Early identification of high-risk patients for cardiovascular disease is critical for reducing morbidity and improving treatment outcomes. This study applies supervised machine learning techniques to predict heart disease using the publicly available Kaggle heart failure dataset, which comprises 918 observations with demographic, clinical, and laboratory attributes, including age, resting blood pressure, cholesterol level, fasting blood sugar, maximum heart rate achieved, ST depression induced by exercise (Oldpeak), and electrocardiographic and chest pain characteristics. The dataset was pre-processed using a unified pipeline that standardized numerical features and encoded categorical variables via one-hot encoding. The data were split into training and testing sets using an 80/20 stratified approach. Three classification algorithms like Logistic Regression, Random Forest, and Support Vector Machine (SVM) with a radial basis function kernel were evaluated using accuracy, precision, recall, F1-score, and ROC–AUC metrics, complemented by confusion matrices and ROC curves. All models demonstrated strong predictive performance, achieving test accuracies of approximately 0.88. The SVM model exhibited the highest discriminative capability, with a ROC–AUC of approximately 0.95, while Logistic Regression achieved the highest recall (≈ 0.93), making it particularly suitable for applications where minimizing false negatives is critical. Correlation analysis identified Oldpeak, maximum heart rate, age, and fasting blood sugar as key factors associated with heart disease. These findings suggest that relatively simple machine learning models, when combined with appropriate preprocessing, can serve as effective decision-support tools for heart disease risk stratification in clinical settings.</p> Dhoha Raad Hussein Ali Subhi Alhumaima Hussein Alkattan Mostafa Abotaleb Copyright (c) 2025 Journal of Transactions in Systems Engineering https://creativecommons.org/licenses/by/4.0 2025-12-29 2025-12-29 4 1 522 537 https://journals.tultech.eu/index.php/jtse/article/view/364 <div><span lang="EN-GB">Quantum Machine Learning (QML) has emerged as a paradigm that combines the computational advantages of quantum computing with the predictive capabilities of machine learning to address complex data classification problems. As data dimensionality increases rapidly and classical learning algorithms face scalability constraints, QML leverages quantum parallelism, entanglement, and high-dimensional Hilbert space representations to enhance learning performance. This paper reviews and analyses advanced QML algorithms, including Quantum Support Vector Machines (QSVM), Variational Quantum Circuits (VQC), Quantum Neural Networks (QNN), and Quantum Kernel Estimation, for optimizing both binary and multi-class classification tasks under high-complexity conditions. The proposed QML framework is evaluated on benchmark datasets like MNIST, the Breast Cancer Wisconsin (BCW) dataset, and synthetic nonlinear datasets, and is compared against classical machine learning baselines, including Support Vector Machines (SVM), Random Forests (RF), and Deep Neural Networks (DNN). The results demonstrate notable improvements in classification accuracy (up to 96.8%), decision margins, and computational efficiency in quantum-suitable data regimes, while also highlighting current limitations related to noise, circuit depth, and hardware constraints. Overall, the study presents a unified QML framework, theoretical formulations, and experimental evaluations that illustrate the potential of quantum algorithms for next-generation classification tasks.</span></div> Ramya Mandava Gullapalli Lasya Sravanthi Copyright (c) 2025 Journal of Transactions in Systems Engineering https://creativecommons.org/licenses/by/4.0 2026-01-01 2026-01-01 4 1 538 559