## 📄️ 6.1 Regressions for Healthcare Data

The application of regression analyses in healthcare presents a unique set of challenges and opportunities. Given the intricacies and complexities inherent in health data, traditional regression techniques may need adjustments or refinements to cater to the specific nature of such data. In this section, we will explore the nuances of using regressions in the context of healthcare, and shed light on the considerations that must be made when analyzing health data.

## 📄️ 6.2 Simple Linear Regression for Health Data

Introduction to Simple Linear Regression

## 📄️ 6.3 Multiple Regression

Introduction to Multiple Regression

## 📄️ 6.4 Polynomial Regression in Health Informatics

Introduction to Polynomial Regression

## 📄️ 6.5 Logistic Regression for Disease Prediction

This section introduces Logistic Regression and demonstrates its application in disease prediction within the context of health informatics. The case study focuses on predicting diabetes based on clinical measurements, illustrating how logistic regression models the probability of a binary outcome. It covers data collection, fitting the logistic regression model, model interpretation, model evaluation (including ROC curve and AUC), clinical implications, and practical considerations. The case study showcases how Logistic Regression aids in disease prediction and informs clinical decision-making.

## 📄️ Resources

- StatsModels for Clinical Research