Previously submitted to: JMIR Medical Informatics (no longer under consideration since Apr 15, 2025)
Date Submitted: Oct 21, 2024
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Cardiovascular Disease Detection: A Hybrid Machine Learning-AI Framework for Personalized Diagnosis and Risk Assessment
ABSTRACT
Background:
Cardiovascular diseases are current number one killers globally, underlining the importance of application of the more accurate diagnostic and therapeutic tools. Traditional screening procedures usually do not provide identification and guidance based on individual peculiarities that might result in less than beneficial results.
Objective:
This study seeks to create a combined machine learning and artificial intelligence model that enables early diagnosis and risk assessment beyond various profiling of patient.
Methods:
A mathematical model was developed to provide the framework to deal with diagnostic complexity of cardiovascular diseases. Artificial intelligence (AI) and machine learning (ML) for prediction and optimization is proposed. The framework employs a variety of forms of patient data namely electronic health records, medical images and genomic data in order to construct patient models. The proposed model includes employing algorithms and approaches such as Support Vector Machine (SVM) and Particle Swarm Optimization (PSO) for prognosis of disease, selecting patients for high risk to early assessing treatment to reduce costs associated with ineffective or delayed treatments, and improves quality of life and outcomes for patients.
Results:
Initial results on the datasets show the proposed framework achieves better performance than previous approaches by an increase in Precision, Recall, Sensitivity, Specificity Negative Likelihood Ratio and AUC. Due to this, the model can predict the disease prognosis, indicate the patients at high risk, and help build the best treatment strategies.
Citation
Request queued. Please wait while the file is being generated. It may take some time.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.