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Cost Analysis Forecast on Vendor Services
Logistics Regression
The given R code snippet outlines the process of creating a logistic regression model to analyze the relationship between various predictors and the likelihood of osteoporosis. This model is built using the glm function, which stands for Generalized Linear Model, suitable for fitting generalized linear models including logistic regression.
This formula specifies that Osteoporosis (the dependent variable) is predicted by a combination of explanatory variables: Age, Gender, Family History, Race/Ethnicity, Vitamin D Intake, Smoking, Medical Conditions, Medications, Prior Fractures, and Hormonal Changes.
The data for fitting the model is taken from a dataset named train. The Osteoporosis dataset likely contains observations (rows) and the specified variables (columns) that are necessary for the model.
The family parameter specifies the type of model to be fitted. Here, binomial indicates that a logistic regression is being performed. Logistic regression is used when the dependent variable is binary (in this case, the presence or absence of osteoporosis).
Random Forest Model and Feature Importance Analysis
I then fitted a Random Forest model to predict osteoporosis and assess the importance of various features contributing to the predictions. The Random Forest model is a popular machine learning algorithm known for its robustness and accuracy, especially useful in classification tasks like predicting disease occurrence based on multiple predictors.
The conclusion drawn from the Random Forest model analysis indicates the ranking and significance of each feature in predicting osteoporosis. Understanding these key predictors allows for a targeted approach in healthcare settings, focusing on significant risk factors for better management and prevention strategies. This approach is valuable in medical research and practice, where identifying and prioritizing risk factors can lead to more effective interventions and improved patient outcomes.
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