A Machine Learning-based Framework for Medical Decision Support Systems
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Abstract
Due to the complexity of clinical decision-making and the necessity for patient-specific suggestions, medical decision support systems (MDSS) are becoming more significant in healthcare. Machine learning (ML) can analyze and learn from vast volumes of patient data, making it a strong tool for MDSS development. Domain specialists, data scientists, and software engineers must work together to produce MDSS. This study provides a Machine Learning-based MDSS development approach that prioritizes stakeholder collaboration. Medical Decision Support Systems (MDSS) provide tailored recommendations based on clinical guidelines, medical expertise, and patient data to improve patient outcomes and healthcare delivery. Machine learning (ML) can analyze and learn from vast volumes of patient data, making it a strong tool for MDSS development. This study provides a Machine Learning-based MDSS development framework with data preparation, feature selection and extraction, model training and evaluation, and integration and deployment. To ensure MDSS accuracy and efficacy, domain experts, data scientists, and software engineers collaborate.
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