An Optimisation-Driven Prediction Method for Automated Diagnosis and Prognosis

open access articleThis article presents a novel hybrid classification paradigm for medical diagnoses and prognoses prediction. The core mechanism of the proposed method relies on a centroid classification algorithm whose logic is exploited to formulate the classification task as a real-valued optimisation problem. A novel metaheuristic combining the algorithmic structure of Swarm Intelligence optimisers with the probabilistic search models of Estimation of Distribution Algorithms is designed to optimise such a problem, thus leading to high-accuracy predictions. This method is tested over 11 medical datasets and compared against 14 cherry-picked classification algorithms. Results show that the proposed approach is competitive and superior to the state-of-the-art on several occasions

On the detection of myocardial scar based on ECG/VCG analysis

In this paper, we address the problem of detecting the presence of myocardial scar from standard ECG/VCG recordings, giving effort to develop a screening system for the early detection of scar in the point-of-care. Based on the pathophysiological implications of scarred myocardium, which results in disordered electrical conduction, we have implemented four distinct ECG signal processing methodologies in order to obtain a set of features that can capture the presence of myocardial scar. Two of these methodologies: a.) the use of a template ECG heartbeat, from records with scar absence coupled with Wavelet coherence analysis and b.) the utilization of the VCG are novel approaches for detecting scar presence. Following, the pool of extracted features is utilized to formulate an SVM classification model through supervised learning. Feature selection is also employed to remove redundant features and maximize the classifier's performance. Classification experiments using 260 records from three different databases reveal that the proposed system achieves 89.22% accuracy when applying 10- fold cross validation, and 82.07% success rate when testing it on databases with different inherent characteristics with similar levels of sensitivity (76%) and specificity (87.5%)

Advancing Chronic Respiratory Disease Care with Real-Time Vital Sign Prediction

Cardiovascular and chronic respiratory diseases, being pervasive in nature, pose formidable challenges to the overall well-being of the global populace. With an alarming annual mortality rate of approximately 19 million individuals across the globe, these diseases have emerged as significant public health concerns warranting immediate attention and comprehensive understanding. The mitigation of this elevated mortality rate can be achieved through the application of cutting-edge technological innovations within the realm of medical science, which possess the capacity to enable the perpetual surveillance of various physiological indicators, including but not limited to blood pressure, cholesterol levels, and blood glucose concentrations. The forward-thinking implications of these pivotal physiological or vital sign parameters not only facilitate prompt intervention from medical professionals and carers, but also empower patients to effectively navigate their health status through the receipt of pertinent periodic notifications and guidance from healthcare practitioners. In this research endeavour, we present a novel framework that leverages the power of machine learning algorithms to forecast and categorise forthcoming values of pertinent physiological indicators in the context of cardiovascular and chronic respiratory ailments. Drawing upon prognostications of prospective values, the envisaged framework possesses the capacity to effectively categorise the health condition of individuals, thereby alerting both caretakers and medical professionals. In the present study, a machine-learning-driven prediction and classification framework has been employed, wherein a genuine dataset comprising vital signs has been utilised. In order to anticipate the forthcoming 1-3 minutes of vital sign values, a series of regression techniques, namely linear regression and polynomial regression of degrees 2, 3, and 4, have been subjected to rigorous examination and evaluation. In the realm of caregiving, a concise 60-second prognostication is employed to enable the expeditious provision of emergency medical aid. Additionally, a more comprehensive 3-minute prognostication of vital signs is utilised for the same purpose. The patient's overall health is evaluated based on the anticipated vital signs values through the utilisation of three machine learning classifiers, namely Support Vector Machine (SVM), Decision Tree and Random Forest. The findings of our study indicate that the implementation of a Decision Tree algorithm exhibits a high level of accuracy in accurately categorising a patient's health status by leveraging anomalous values of vital signs. This approach demonstrates its potential in facilitating prompt and effective medical interventions, thereby enhancing the overall quality of care provided to patients

Heart Disease Detection using Vision-Based Transformer Models from ECG Images

Heart disease, also known as cardiovascular disease, is a prevalent and critical medical condition characterized by the impairment of the heart and blood vessels, leading to various complications such as coronary artery disease, heart failure, and myocardial infarction. The timely and accurate detection of heart disease is of paramount importance in clinical practice. Early identification of individuals at risk enables proactive interventions, preventive measures, and personalized treatment strategies to mitigate the progression of the disease and reduce adverse outcomes. In recent years, the field of heart disease detection has witnessed notable advancements due to the integration of sophisticated technologies and computational approaches. These include machine learning algorithms, data mining techniques, and predictive modeling frameworks that leverage vast amounts of clinical and physiological data to improve diagnostic accuracy and risk stratification. In this work, we propose to detect heart disease from ECG images using cutting-edge technologies, namely vision transformer models. These models are Google-Vit, Microsoft-Beit, and Swin-Tiny. To the best of our knowledge, this is the initial endeavor concentrating on the detection of heart diseases through image-based ECG data by employing cuttingedge technologies namely, transformer models. To demonstrate the contribution of the proposed framework, the performance of vision transformer models are compared with state-of-the-art studies. Experiment results show that the proposed framework exhibits remarkable classification results