Logistic Regression [Biostatistics & Machine Learning] 

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In the realm of disease research within biology, logistic regression serves as a fundamental machine learning algorithm for predictive modeling and understanding the relationship between various factors and disease outcomes. It's particularly valuable in scenarios where the outcome of interest is binary, such as disease presence or absence.
Researchers select relevant biological and clinical features (independent variables), such as genetic markers, biomarkers, demographic information, or environmental factors, that could potentially influence the likelihood of disease occurrence.
It allows for hypothesis testing and inference about the significance of individual features in predicting disease outcomes.
• Cancer Risk Prediction: Identifying genetic or environmental factors associated with increased risk of cancer.
• Disease Diagnosis: Developing diagnostic models for infectious diseases based on clinical symptoms and laboratory tests.
• Drug Response Prediction: Predicting individual patient response to specific medications based on genetic or molecular profiles.
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