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Explanation: Logistic regression is a statistical method that models the relationship between a dependent variable and one or more independent variables using a logistic function. In healthcare, it is particularly valuable because it is used for binary classification tasks , such as predicting whether a patient has a certain disease (yes/no) or if a recovery will occur (yes/no). This is ideal for healthcare applications like diagnosis prediction or determining whether patients will need intensive care. The output of logistic regression is a probability score between 0 and 1, which makes it highly interpretable and suitable for binary outcomes, like disease diagnosis or treatment response. Option A: Linear regression is used for predicting continuous outcomes (e.g., price, temperature), not binary outcomes like disease presence. Option C: Time series analysis is typically used for forecasting data over time and would not directly be applied to binary outcome predictions like a diagnosis. Option D: SVM is a robust classifier, but logistic regression is often preferred for binary outcomes in medical fields due to its simplicity and interpretability. Option E: Decision Trees could also be used for classification, but logistic regression generally gives better probability outputs that are more interpretable in healthcare predictions.
Who is the regulator of the corporate sector?
With respect to AS: 16 (Borrowing Costs), which of the following statement is incorrect?
What should be the correct Journal Entry for booking premium income in case of Incoming coinsurance:
Match the following: