Project Summary
A health insurance company can only make money if it collects more than it spends on the medical care of its beneficiaries. On the other hand, even though some conditions are more prevalent for certain segments of the population, medical costs are difficult to predict since most money comes from rare conditions of the patients. The objective of this article is to accurately predict insurance costs based on people’s data, including age, Body Mass Index, smoking or not, etc. Additionally, we will also determine what the most important variable influencing insurance costs is. These estimates could be used to create actuarial tables that set the price of yearly premiums higher or lower according to the expected treatment costs. This is a regression problem.
Predicting healthcare costs for individuals using accurate prediction models is important for various stakeholders beyond health insurers, and for various purposes4. For health insurers and increasingly healthcare delivery systems, accurate forecasts of likely costs can help with general business planning in addition to prioritizing the allocation of scarce care management resources. Moreover, for patients, knowing in advance their likely expenditures for the next year could potentially allow them to choose insurance plans with appropriate deductibles and premiums.
Project Overview: Utilized Python to develop machine learning models to accurately forecast future medical expenses for individuals based on historical health data. By leveraging Python’s advanced data analysis and machine learning libraries, the project sought to provide healthcare providers and insurance companies with valuable insights for budgeting, risk management, and personalized patient care.
- DELIVERABLES
- Utilized Python in developing a machine learning model to predict medical costs, leveraging advanced algorithms to provide accurate estimates for healthcare budgeting and planning.
- Collected and preprocessed a comprehensive dataset that included patient demographics, medical history, treatment details, and insurance information to ensure robust model training.
- Utilized various machine learning algorithms, including linear regression, and ensemble methods like Random Forests, to evaluate and select the best-performing model.
- Implemented cross-validation and hyperparameter tuning to optimize model performance, achieving high accuracy and minimizing prediction errors.
- Conducted sensitivity analysis to understand the impact of different features on medical cost predictions, guiding feature selection and model refinement.
- Provided actionable insights and recommendations based on model predictions to support cost management strategies and improve financial planning in healthcare organizations.
- Compared various regression models to arrive at a model with an accuracy of 90%.
- ANALYSIS IMPACT
- Successfully delivered a suite of machine learning models that accurately predict medical costs, offering valuable insights for budgeting, risk management, and personalized healthcare. The interactive dashboards and detailed reports will provide stakeholders with clear and actionable information, enhancing decision-making and strategic planning.