The cost of medical care significantly impacts both healthcare providers and patients. This project aims to explore the predictive utility of patient features captured by an insurance firm to estimate the annual cost of medical care. The dataset used is the publicly available Medical Cost Personal dataset from Kaggle, containing information on 1338 beneficiaries and 7 variables, including the target variable: medical costs billed by health insurance in a year.In this study, we aim to build upon previous work by applying advanced techniques to improve the accuracy of predictions and enhance model interpretability.

Chronic Obstructive Pulmonary Disease (COPD) is a prevalent lung condition associated with significant morbidity and mortality worldwide. Predicting the quality of life (QOL) of COPD patients is crucial for treatment planning and improving patient outcomes. In this project, we explore the application of multiple linear regression (MLR) models to predict the QOL of COPD patients based on various patient characteristics. We demonstrate how MLR models can serve as valuable tools in clinical research and public health by providing insights into disease severity and patient outcomes.

This project establishes a foundational framework for predicting medical costs using multiple linear regression. It establishes the potential of certain patient features in the dataset used, for predicting the annual medical costs billed from the perspective of the insurer. The upcoming Part II of the study will delve into implementing recommendations for further model improvement. Connect with the author, Michael Adu, PharmD, on LinkedIn: [Michael Adu](https://www.linkedin.com/in/drmichael-adu). *Disclaimer: The study is designed for learning and research purposes, providing insights into linear regression modeling in healthcare. Findings are not intended for commercial or diagnostic use.*