This task delves into the application of generalized linear models (GLM), particularly logistic regression, in analyzing categorical data related to maternal healthcare.
The dataset under scrutiny contains comprehensive information regarding maternal health care, encompassing various factors such as province, education level, wealth status, insurance coverage, media exposure, maternal age, ethnic diversity, desire for pregnancy, hospital delivery, recent childbirth history, child mortality, decision-making dynamics, occupation, household demographics, and socio-cultural attributes.
The primary objective is to develop a robust classification model using logistic regression, leveraging the aforementioned variables or a judiciously selected subset thereof.
The performance of the model will be meticulously assessed using the Receiver Operating Characteristic (ROC) curve, which provides insights into the trade-off between true positive rate and false positive rate.
Through this analysis, the task aims to identify the most effective model for predicting maternal health outcomes, thereby contributing to the advancement of healthcare planning and policy formulation. The methodology draws upon established literature on logistic regression (Hosmer Jr et al., 2013; Agresti, 2002) and ROC analysis (Fawcett, 2006; Zweig & Campbell, 1993).
It seems like you’re outlining the methods you plan to use for your analysis. Let me provide a brief explanation of each:
Logistic Regression: This is a statistical method used for modeling the probability of a binary outcome. It’s commonly used when the dependent variable is dichotomous (e.g., success/failure, yes/no). Logistic regression estimates the probability that a given observation belongs to one of the categories based on one or more independent variables. It’s useful for understanding the relationship between the independent variables and the probability of a particular outcome.
Receiver Operating Characteristic (ROC):
ROC analysis is a method for evaluating the performance of a classification model. It plots the true positive rate (sensitivity) against the false positive rate (1-specificity) for different threshold values. The area under the ROC curve (AUC) is often used as a measure of how well the model can distinguish between the classes. A higher AUC indicates better discrimination between positive and negative cases.
In your case, you plan to use ROC analysis to evaluate the accuracy of your logistic regression model. By examining the ROC curve and calculating the AUC, you can assess how well the model predicts the binary outcome. This can help you determine the effectiveness of the model and choose the most accurate predictive model among alternatives.
In this section the following will be covered:
Use of caret library and CreateDatapartition method to split data into train and testing sets 80/20 rule will be used
Fit a model based on the training set
Predict how the fitted model performs on the testing set
Establish a cut off to help classify the outcome as either positive or negative
Apply an ROC curve and confusion matrix on the predicted and the observed values