Publication
2ARTs – Decision Support System for Exercise and Diet Prescriptions in Cardiac Recovery Patients
| datacite.subject.fos | Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática | pt_PT |
| dc.contributor.advisor | Martinho, Ricardo Filipe Gonçalves | |
| dc.contributor.advisor | Rijo, Rui Pedro Charters Lopes | |
| dc.contributor.advisor | Grilo, Carlos Fernando de Almeida | |
| dc.contributor.author | Pereira, Andreia Alexandra Sousa | |
| dc.date.accessioned | 2024-05-14T15:40:11Z | |
| dc.date.available | 2024-05-14T15:40:11Z | |
| dc.date.issued | 2023-12-04 | |
| dc.description.abstract | The global health care system is faced with a variety of complicated challenges, ranging from limited access and increasing expenses to an aging population causing increased pressure on healthcare systems. Healthcare professionals are seeking alternative approaches to provide fair access and sustain high-quality care for everyone as a result of these challenges. Patients have historically been restricted from accessing essential healthcare services due to traditional barriers like geographic distance, financial and resource limitations. Innovative solutions to these problems are starting to take shape, thanks to the growth of eHealth platforms that use technology to improve patient care. Through a comprehensive study of existing solutions in the healthcare domain, particularly in cardiology, we identified the need for a Decision Support System (DSS) that would empower physicians with valuable insights and facilitate informed physical and diet prescribing practices into Cardiac Rehabilitation Programmes (CRPs). The major goal of 2ARTs’ project is to create and implement a cardiac rehabilitation platform into a hospital's infrastructure. A key aspect of this platform is the integration of a decision support system designed to provide physicians with valuable information when prescribing individualized treatment prescriptions for each patient, minimizing the potential of human error. The DSS uses algorithms and predictive models to classify patients into distinct groups based on their features and medical history. This classification provides critical insights and additional knowledge to doctors, allowing them to make informed judgments regarding the most effective treatment options for each patient's cardiac rehabilitation journey. By using the power of data-driven analytics and machine learning, the DSS enables doctors to better understand each patient's needs and personalize treatment actions accordingly. In order to achieve the best possible results aligned with the goals of the project, a variety of approaches based on comprehensive studies were explored, specifically feature selection and feature reduction methods, where their performance metrics were evaluated, seeking the most effective solution. It was through this thorough analysis that Principal Component Analysis (PCA) emerged as the standout choice. PCA not only demonstrated superior outcomes in evaluation metrics, but also showcased excellent compatibility with the selected clustering algorithm along with the best results after an expert analysis. Moreover, with the analysis of the data types and features the dataset had, the K-Means algorithm produced the best results and was more adaptable to our dataset. We were able to identify useful insights and patterns within the data by employing both PCA and K-Means, opening the way for more accurate and informed decision-making in the 2ARTs project. | pt_PT |
| dc.identifier.tid | 203607961 | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10400.8/9663 | |
| dc.language.iso | eng | pt_PT |
| dc.subject | Programa de Reabilitação Cardíaca | pt_PT |
| dc.subject | Algoritmos de Clustering | pt_PT |
| dc.subject | Sistemas de Apoio à Decisão | pt_PT |
| dc.subject | eHealth | pt_PT |
| dc.subject | Machine Learning | pt_PT |
| dc.subject | Modelos Preditivos | pt_PT |
| dc.title | 2ARTs – Decision Support System for Exercise and Diet Prescriptions in Cardiac Recovery Patients | pt_PT |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | masterThesis | pt_PT |
| thesis.degree.name | Mestrado em Engenharia Informática - Computação Móvel | pt_PT |
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