Semantic Grouping of Features for Predictive Modeling in Machine Learning

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Background Semantic grouping of features refers to organizing related features into meaningful clusters based on their inherent relationships or domain-specific knowledge. In predictive modeling, this process is crucial for improving model interpretability, performance, and robustness. By clustering features that share a common context or meaning, we can reduce dimensionality, simplify feature engineering, and potentially enhance…

Personalization of treatment based on biomarkers

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Definition Central to the personalized medicine approach is the utilization of biomarkers—measurable indicators of biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention. Biomarkers enable clinicians to make informed decisions about diagnosis, prognosis, and therapy selection, enhancing treatment efficacy and patient outcomes. Pharmacodynamic Biomarkers Pharmacodynamic biomarkers are crucial for assessing a drug’s biological…

Blood exam classification for predicting defining factors in metabolic syndrome diagnosis and other related conditions

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Abstract In this paper, we reported on recent findings from our research on investigating the link between a person’s standard biochemistry profile (based on blood exams), body mass index (BMI), metabolism as health state and Systolic Blood Pressure (SBP). Our current findings expand upon our previous related research, which was based on the use of…